Reviewerly/exHarmony huggingface dataset:
Efficient Implementation of Pooling Operation for AI Accelerators

Deep neural networks (DNNs) are extensively used in artificial intelligence (AI) applications which often have stringent design constraints. To enable large scale deployment of DNNs in AI applications it is imperative to design and implement efficient algorithms and hardware architectures for DNNs. Most neural network hardware accelerators prioritize in optimizing the execution of the convolutional layers as opposed to the pooling layers, resulting in performance degradation of the accelerator and increase in the system power consumption. This paper explores efficient hardware architectures for processing pooling layers by focusing on mitigating memory access bottlenecks by vectorizing the pooling operations along the channel dimension of the activation matrix. The proposed hardware provides performance improvement of approximately 200x as compared to the software implementation, subjected to simulation environment constraints. Similar observation can be extrapolated for the reduction in power consumption with this proposed pooling hardware architecture.
ANALISIS KESUKSESAN SIAP BOS (SISTEM INFORMASI APLIKASI PENGELOLAAN BANTUAN OPERASIONAL SEKOLAH) DI KABUPATEN BLORA

&lt;p&gt;&lt;em&gt;The SIAP BOS (Sistem Informasi Aplikasi Pengelolaan Bantuan Operasional Sekolah) application is an online-based application containing budgeting, administration, accountability, and reporting of BOS funds developed by the Blora District Education Office with a third party (PT. Solusi Teknologi Informasi/ Solusiti). This study aims to determine the success of the SIAP BOS application using the DeLone and McLean information system success model. The variables used are system quality, information quality, service quality, user satisfaction, and net benefits. The population of this study is the SIAP BOS application users including principals, BOS treasurers, BOS operators of State Junior High School (SMP in Blora Regency and the BOS Team of the Blora Regency Education Office with a sample of 120 people. Collecting data through the distribution of online questionnaires with a google form. Data of 120 respondents were collected then analyzed by Partial Least Square (PLS) using the SmartPLS 3.3.3 application. Based on the results of the study, it shows that system quality, information quality, and service quality have a positive effect on user satisfaction, and user satisfaction has a positive effect on net benefits.&lt;/em&gt;&lt;/p&gt;
Silicon Photonics for Training Deep Neural Networks

Analog photonic networks as deep learning hardware accelerators are trained on standard digital electronics. We propose an on-chip training of neural networks enabled by a silicon photonic architecture for parallel, efficient, and fast data operations.
Simulação de Transitórios Eletromecânicos e Análise da Estabilidade a Pequenas Perturbações através de ferramentas desenvolvidas em Python

Computer simulations are an important tool for the expansion and operation of electrical systems. Within the simulations carried out for the study of electrical systems, there is the simulation of electromechanical transients, and this type of simulation is extremely important for the study of stability in electrical systems. This work aims to show the development of two electrical systems simulation codes written in Python language, one dedicated to time simulation of electromechanical transi1ents and the other to the analysis of angular stability at small disturbances. These codes were applied in the simulation of a 14-bus system, presenting results that converge with traditional software.
Effect of online civic intervention and online disinhibition on online hate speech among digital media users

Given the numerous theoretical gaps in explanations regarding online hate speech and the insufficient level of empirical data to fully understand this phenomenon, this study utilized an explanatory sequential mixed-method approach through two phases; it employed a quantitative online questionnaire (n=2,242), followed by a qualitative online vignette interview (n=23) to contribute to the knowledge in the field. In particular, it aimed to investigate the moderating roles of online civic intervention (OCI), online disinhibition, and demographic variables in the relationship between online hate exposure\victimization and perpetration. Among the most interesting findings of this research was that the impact of toxic online disinhibition was a negative moderator in the relationship between online hate exposure and perpetration. Furthermore, the impact of high-threshold OCI was positive in this relationship. However, the impact of low-threshold OCI was positive in the relationship between online hate speech victimization and perpetration. Further justifications for OCI and new proposed types of online disinhibition have been suggested based on the findings.
Program State Visualizer with User-Defined Representation Conversion (WIP)

Conventional non-visual tree-based debuggers possess comprehensibility issues, which include the obscurity of object references, patterns, and overall program structure. Visual debuggers — specifically, ones that display an object diagram to represent the program state — alleviate these issues for imperative programming languages whose very states are directly manipulated by the programmer’s statements. However, these debuggers are also prone to clutter when representing large program states from visualizing too much information. Additionally, the visualized program state can often differ from its conceptual abstraction on paper. We propose user-defined representation conversion which allows users to convert concrete representations of program states to their more focused and abstracted conceptual versions. We design a DSL such that users can specify conversions to manipulate displayed nodes and edges based on object types, references, values, or debugger halt locations. We implemented a prototype of this concept for Java.
Using Object-Sequence Diagrams for Debugging

Programming languages in general have mechanisms for describing the structure of a program and computational processes to be generated during program execution. Modeling languages like UML also have such mechanisms. In addition, UML has mechanisms for describing required properties of a system using e.g. objects diagrams, and sequence diagrams, during the design process. In this paper, we propose to include such language mechanisms also for programming with the purpose of observing program executions. We present a combination of sequence diagrams and object diagrams called object sequence diagrams (OSDs). We present an OSD-based debugger that has the same functionality as a traditional debugger, but method invocations are shown in sequence diagrams. The debugger supports showing the history of method invocations and supports debugging at different levels of the program structure, by hiding method invocations within nested modules.
"F O R M S" - Creating new visual perceptions of dance movement through machine learning

"FORMS" is a new digital art concept that combines the fields of dance movement and machine learning techniques, specifically human pose detection, to create a real-time and interactive visual experience. This project aims to explore the relationship between dance and visual art by creating a framework that generates abstract and literal visual models from the dancers’ movements. The main objective of this project is to enhance the perception of dance movement by providing a new layer of visual composition. The proposed framework provides different visual forms based on human pose detection, creating a novel and real-time visual expression of the dance movement. The human pose detection model used in this project is based on state-of-the-art deep learning techniques, which analyze the positions and movements of different parts of the human body in real-time. This model allows the framework to capture movements of the dancers and translate them into unique visual forms. The case study showcases the potential of "FORMS" by demonstrating how professional young dancers can use the framework to enrich their performance and create new visual perceptions of dance movement. This study contributes to the cultivation of body awareness, understanding of the dance movement and overall enrichment of the art experience. The use of machine learning techniques showcases the potential of technology to enhance and expand the boundaries of artistic expression. The "FORMS" project is a novel and interdisciplinary approach that bridges the fields of art and technology, providing a new way to experience and perceive the dance movement.
Preserving Virtual Reality

According to the Library of Congress, 75% of silent movies before the 1930s are lost forever. [Pierce, 2013] During the early 20th century, films were not considered to have much future value or lasting significance which lead to the majority of them not being properly archived or preserved. Most films were intentionally destroyed to avoid spending money on storage space and expensive upkeep of the materials. [Pierce, 2013] By the time studios and institutions started to establish film archives, it was already too late for most of early cinema. We are seeing a similar scenario take place with preserving interactive media projects as the majority of VR experiences have been ignored by institutional archives. Preserving virtual reality has a particularly unique challenge because it is designed with interface, hardware, and software in mind. Unlike films and games that can easily be archived in formats that can be universally played on screens, the majority of virtual reality experiences are designed to run on very specific hardware. Once a headset is no longer supported, the dedicated VR experiences also disappear with it, unless they are continuously updated to move to newer systems. Unfortunately, the majority of independent developers and creators do not have the resources, time, or budget to keep up with the constant turnover rate of new hardware. Not to mention the lack of infrastructure that prevents content creators from ensuring the longevity of titles that are not positioned with a financial gain.
Music, Motion, and Mixed Reality: An Interdisciplinary, Problem-Based Educational Experience

This talk describes an interdisciplinary educational experience involving cohorts of students studying Computer Science, Dance, 3D Digital Design and Music. The experience takes a problem based learning approach towards a target goal of presentation of a set of live performances that combine music and dance with augmented reality. Our overall approach of integration of the varying disciples as well as impressions of students involved in the experience is discussed.
How Close Are You to Your End Consumer?

The delivery of a tailored customer experience is being widely recognised by executives in the technology industry as the key to unlocking revenue, minimising attrition, and providing growth. It is not simple to satisfy a consumer in today's market. Delivering reliable and efficient experiences across channels is more challenging than it has ever been because of the context of disparate privacy regulations, quick updates to browser technology, and an ever-evolving technological landscape. This research suggests that to do it right, the business needs to have the right people, processes, and technologies working in sync. This study highlights that many companies continue to invest in instruments and technology solutions before they have effectively accomplished the organisational transformation required to perform the role in a data-driven mode. Investments do not always yield the promised results since the basic pieces of mindset, vision, and people are not always in congruence. Consumers are no longer going to be ‘just satisfied,' or ‘even happy.'
Can Metaverse Act as a Board of Directors?

The recent pandemic has had a negative impact on the market and related industries, primarily due to the forced closure of businesses by governments. However, the main cause of this impact is ineffective organizational strategies and their poor implementation. In today's business environment where profitability is the sole focus of management, there is a lack of strategy creation and implementation. In this scenario, the board of directors needs an additional resource to provide precise and well-researched information. This resource can be called the metaverse. Metaverse can be used in the corporate world to improve various processes and operations and provides assistance to make effective decisions. This chapter applies agency theory to show how the metaverse can act as a board member and improve the effectiveness of development strategies. This chapter introduces a new concept for organizational governance. It will be useful for regulators, policy makers, and boards of directors.
Analysis of Multi-layer Information Flow in a Four-Layer Blockchain Model

The blockchain technology allows nowadays to decentralise information and the use of cryptographic techniques make this technology secure and reliable thus adding functionality for sharing information through smart contracts among other functionalities. One of the main disadvantages of this process is scalability in the speed of transactions making the systems slow for practical applications. In this paper, a multilayered approach is proposed in order to address the problem of improving speed in transactions; the method is presented by means of a case study analysis, by dividing the information in four-layers based on blockchain which allow users to participate and contribute to decentralise information thus making the service more reliable. The main contribution in this paper is the analysis that shows interaction for sharing information between different layers thus monitoring the behaviour when transactions are being made.
Have the cake and eat it too: Differential Privacy enables privacy and precise analytics

Abstract Existing research in differential privacy, whose applications have exploded across functional areas in the last few years, describes an intrinsic trade-off between the privacy of a dataset and its utility for analytics. Resolving this trade-off critically impacts potential applications of differential privacy to protect privacy in datasets even while enabling analytics using them. In contrast to the existing literature, this paper shows how differential privacy can be employed to precisely—not approximately—retrieve the analytics on the original dataset. We examine, conceptually and empirically, the impact of noise addition on the quality of data analytics. We show that the accuracy of analytics following noise addition increases with the privacy budget and the variance of the independent variable. Also, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual data to which we add Laplace noise, we provide evidence supporting these two predictions. We then demonstrate our central thesis that, once the privacy budget employed for differential privacy is declared and certain conditions for noise addition are satisfied, the slope parameters in the original dataset can be accurately retrieved using the estimates in the modified dataset of the variance of the independent variable and the slope parameter. Thus, differential privacy can enable robust privacy as well as precise data analytics.
Digital Skills of Teachers

The digital age of the twenty-first century requires the use of information and communication technology (ICT). The development of the digital world is opening up a wide range of new possibilities for sharing and collaboration across numerous industries. Digital competence is one of the crucial skills that instructional staff members must possess. The necessity for the adequate acquisition of novel information capabilities in the information society has been one of the key forces behind curriculum change. Although digital technologies are increasingly being used in education, it's important to understand how they might be used to progress educational reform and enhance effective pedagogical techniques. Teachers should be mindful that technology will affect the teaching strategies they employ. The transfer of 21st-century core competencies requires that instructors exhibit digital proficiency. The development of digital teaching abilities is a challenge for teacher education needs to be a priority for the education institutes should be a priority in education public policy.
Scientist and his professional identity

The research considers the characteristics of the professional identity of a modern Russian scientist. We provide a definition for the professional identity notion and define the problems the scientists face in the course of their activity. In particular, these problems are associated with a vague hierarchy of occupational roles and a poor command of foreign languages that may create obstacles for the professional development and the formation of strong professional identity. Finally, we draw the conclusions on the factors affecting the formation of a strong professional identity in scientists.
Relinearization Attack On LPN Over Large Fields

Abstract We investigate algebraic attacks on the Learning Parity with Noise ($\mathsf{LPN}$) problem over large fields in parameter settings relevant to building indistinguishability obfuscation in which the proportion of corrupted equations is inverse-polynomially sparse. Our aim was to obtain a subexponential algorithm using the Macaulay expansion and relinearization. Alas, we did not. Nevertheless, our findings suggest an interesting relation between runtime and the rank of the Macaulay expansion. The runtime of this attack is $O\big(2^{d \log m}\big)$, where $m$ is the number of initial equations and $d$ is the degree of the Macaulay expansion. If the resulting system of equations has sufficiently large rank, we show that solving the $\mathsf{LPN}$ polynomial system requires an $O(\sqrt{m})$ degree expansion, which would imply a subexponential attack. Under the (more widely believed) assumption that the expanded system is semi-regular, however, we show that an $O(m)$ degree expansion is required to recover the secret vector. Since $O(\sqrt{m})$-degree expansions may not have sufficient rank, we propose a randomized algorithm which introduces carefully chosen equations that hold with high probability to increase the rank and improve the likelihood of a successful attack. We highlight the empirical and theoretical challenges in analyzing this approach. Our code is available at www.tinyurl.com/attacklpn.
Deep Transfer Learning for Nanophotonic Device Design

Applying a transfer-learning technique for generative deep neural networks, we demonstrate a very time-efficient inverse design framework for photonic integrated circuit devices, when there are new demands for structural/material parameters from an existing device library.
Model-based preference quantification

Any prescriptive theory of decision-making (DM) has to cope with the common DM agents' inability to fully specify their preferences dependent on several attributes. The paper provides the needed preference completion and quantification for fully probabilistic design (FPD) of DM strategies. FPD (covering the usual Bayesian DM) probabilistically models the agent's environment and quantifies its preferences via an ideal probabilistic model of the closed DM loop. The probability density (pd) models (closed-loop) behaviour, a collection of involved random variables. Its ideal twin is high on desired behaviours, small on undesired and zero on forbidden ones. The FPD-optimal strategy minimises the Kullback-Leibler divergence (KLD) of the closed-loop modelling pd to the ideal twin. The exposed preference quantification chooses the optimal ideal pd from the set of pds compatible with partially-specified agent's preferences. The optimal ideal pd minimises the KLD minima reached by the optimal strategies for respective imminent ideal pds. This preference-focused twin of the minimum KLD principle was applied to special sets of ideal pds. The paper extends them towards exploration and balancing contradictory wishes on states and actions.
A Methodological Approach to Verify Architecture Resiliency

Architecture-first approach to address software resiliency is becoming the mainstream development method for mission-critical and software-intensive systems. In such approach, resiliency is built into the system from the ground up, starting with a robust software architecture design. As a result, a flaw in the design of a resilient architecture affects the system’s ability to anticipate, withstand, recover from, and adapt to adverse conditions, stresses, attacks, or compromises on cyber-resources. In this paper, we present an architecture-centric reasoning and verification methodology for detecting design weaknesses in resilient systems. Our goal is to assist software architects in building sound architectural models of their systems. We showcase our approach with the aid of an Autonomous Robot modeled in AADL, in which we use our methodology to uncover three architectural weaknesses in the adoption of three architectural tactics.
Establishing a Benchmark Dataset for Traceability Link Recovery Between Software Architecture Documentation and Models

In research, evaluation plays a key role to assess the performance of an approach. When evaluating approaches, there is a wide range of possible types of studies that can be used, each with different properties. Benchmarks have the benefit that they establish clearly defined standards and baselines. However, when creating new benchmarks, researchers face various problems regarding the identification of potential data, its mining, as well as the creation of baselines. As a result, some research domains do not have any benchmarks at all. This is the case for traceability link recovery between software architecture documentation and software architecture models. In this paper, we create and describe an open-source benchmark dataset for this research domain. With this benchmark, we define a baseline with a simple approach based on information retrieval techniques. This way, we provide other researchers a way to evaluate and compare their approaches.
No cause for collapse

Abstract We investigate a hitherto under-considered avenue of response for the logical pluralist to collapse worries. In particular, we note that standard forms of the collapse arguments seem to require significant order-theoretic assumptions, namely that the collection of admissible logics for the pluralist should be closed under meets and joins . We consider some reasons for rejecting this assumption, noting some prima facie plausible constraints on the class of admissible logics which would lead a pluralist admitting those logics to resist such closure conditions.