Reviewerly/exHarmony huggingface dataset:
On the complexity of two-party differential privacy

In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-party functions such as inner product and Hamming distance, the accuracy of the distributed solution lags way behind what is achievable in the client-server setting. McGregor, Mironov, Pitassi, Reingold, Talwar, and Vadhan [FOCS ’10] proved that this gap is inherent, showing upper bounds on the accuracy of (any) distributed solution for these functions. These limitations can be bypassed when settling for computational differential privacy, where the data is differentially private only in the eyes of a computationally bounded observer, using oblivious transfer.
Color Channel Fusion Network For Low-Light Image Enhancement

When capturing images in low light condition, due to insufficient lighting, the true color information and texture details of objects are difficult to obtain. Considering that, we propose an end-to-end color channel fusion network (CCFN). Specifically, our proposed method uses partial channel combination inputs to obtain multiple enhancement results. The relevance among RGB channels is maintained by modeling channel interdependencies. Subsequently, a multi-scale feature channel shuffle module (MFCS) is designed to combine image features at different scales, which makes the fusion images hold more rich information. Finally, the output images are generated after detail enhancement. Extensive experiments demonstrate the superiority of our method over several state-of-the-arts in terms of enhancement quality.
Cost-Aware Ant Colony Optimization Based model for Load Balancing in Cloud Computing

: Balancing the incoming data traffic across the servers is termed as Load balancing. In cloud computing, Load balancing means distributing loads across the cloud infrastructure. The performance of cloud computing depends on the different factors which include balancing the loads at the data center which increase the server utilization. Proper utilization of resources is termed as server utilization. The power consumption decreases with an increase in server utilization which in turn reduces the carbon footprint of the virtual machines at the data center. In this paper, the cost-aware ant colony optimization based load balancing model is proposed to minimize the execution time, response time and cost in a dynamic environment. This model enables to balance the load across the virtual machines in the data center and evaluate the overall performance with various load balancing models. As an average, the proposed model reduces carbon footprint by 45% than existing methods.
Research on the Application of Preschool Music Education in Colleges and Universities Based on Network Information Technology

Abstract In order to improve the comprehensive quality of students, promote the all-round development of students, music teaching is an important content of teaching activities. Schools can use the Internet technology, improve the teaching content of music lessons, improve students’ interest in learning music. This paper firstly explains the current situation of music education and the necessity of practical innovation in music education under the background of information age, and at the same time puts forward some strategies for the application of network information technology in preschool music education in colleges and universities for readers’ reference.
Applications of underground structures for the physical protection of critical infrastructure

The U.S. President’s Commission on Critical Infrastructure Protection (PCCIP), convened in the wake of the bombing of the Murrah Federal Building in Oklahoma City, concluded that the nation’s physical security and economic security depend on our critical energy, communications, and computer infrastructures. 1 While a primary motivating event for the establishment of the commission was the catastrophic physical attack of the Murrah Building, it is ironic that the commission focused its attention primarily on cyber threats. Their rationale was that cyber vulnerabilities posed a new, unaddressed challenge to infrastructure security. This approach was further questioned by the events of September 11, 2001 and the subsequent bio-threat events in America. During and shortly after the convention of the President’s Commission on Critical Infrastructure Protection in 1997–99, a working group met to look into the physical protection of critical U.S. infrastructure using underground structures. This “Underground Structures Infrastructure Applications” (USIA) group provided a timely balancing discussion of issues surrounding the physical vulnerability aspect of the infrastructure protection problem. The group convened a workshop on the subject under the auspices of the National Research Council’s Board on Infrastructure and the Constructed Environment. 2 The present paper draws on the working group deliberations and NRC Workshop proceedings to address infrastructure assurance in the context of maximizing the physical protection of high value infrastructure by the use of underground construction. Although the PCCIP did not directly address a role for underground facilities (UGFs), its final report recommended a program of government and industry cooperation and information sharing to improve the physical security of critical United States infrastructure. We develop specific recommendations for the use of underground facilities to support implementation of the general PCCIP challenge. It is our contention that underground facilities reduce the risk of infrastructure disruption from both physical and cyber attacks. In the latter case, undergrounds provide secure reserve operations and back-up data storage locations to ensure the reconstitution of critical electronic information systems. In addition, noting that the Norwegians have placed much of their critical infrastructure underground, we provide information on the Norway’s experience gathered on a fact-finding trip to that country organized by one of the authors. The events of September 11th, 2001 and the use of underground locations for terrorist security by the Taliban, provide a strong impetus for the use of underground construction to protect vital infrastructure.
Agentic Engagement with a Programmable Dialog System

Dialog with a social pedagogical robot or agent is a powerful way for kids to learn [1, 5] but may limit the formation of an agentic relationship with the technology [9]. One main purpose of conversational agents is to allow the user to have a natural interaction that reduces the need to learn artificial conventions [6], but dialog systems fall short with respect to failure recovery, vocabulary diversity, remembering conversational history, and other measures [2, 3]. Further, Hill et. al. [4] found that people adapt their model of communication to match a chatbot’s in the same way they do with a child or non-native speaker. Thus, users conversing with a pedagogical agent are implicitly trained to shape their behavior to suit the technology rather than shaping the technology. For young learners, particularly among populations that have been historically excluded from technology fields, this limits agency and reinforces marginalizing power structures [9].
EmoCat: Language-agnostic Emotional Voice Conversion

Emotional voice conversion models adapt the emotion in speech without changing the speaker identity or linguistic content. They are less data hungry than text-to-speech models and allow to generate large amounts of emotional data for downstream tasks. In this work we propose EmoCat, a language-agnostic emotional voice conversion model. It achieves high-quality emotion conversion in German with less than 45 minutes of German emotional recordings by exploiting large amounts of emotional data in US English. EmoCat is an encoder-decoder model based on CopyCat, a voice conversion system which transfers prosody. We use adversarial training to remove emotion leakage from the encoder to the decoder. The adversarial training is improved by a novel contribution to gradient reversal to truly reverse gradients. This allows to remove only the leaking information and to converge to better optima with higher conversion performance. Evaluations show that Emocat can convert to different emotions but misses on emotion intensity compared to the recordings, especially for very expressive emotions. EmoCat is able to achieve audio quality on par with the recordings for five out of six tested emotion intensities.
Mining text from natural scene and video images: A survey

Abstract In computer terminology, mining is considered as extracting meaningful information or knowledge from a large amount of data/information using computers. The meaningful information can be extracted from normal text, and images obtained from different resources, such as natural scene images, video, and documents by deriving semantics from text and content of the images. Although there are many pieces of work on text/data mining and several survey/review papers are published in the literature, to the best of our knowledge there is no survey paper on mining textual information from the natural scene, video, and document images considering word spotting techniques. In this article, we, therefore, provide a comprehensive review of both the non‐spotting and spotting based mining techniques. The mining approaches are categorized as feature, learning and hybrid‐based methods to analyze the strengths and limitations of the models of each category. In addition, it also discusses the usefulness of the methods according to different situations and applications. Furthermore, based on the review of different mining approaches, this article identifies the limitations of the existing methods and suggests new applications and future directions to continue the research in multiple directions. We believe such a review article will be useful to the researchers to quickly become familiar with the state‐of‐the‐art information and progresses made toward mining textual information from natural scene and video images. This article is categorized under: Algorithmic Development &gt; Text Mining
Terahertz Pulse Shaping Using Diffractive Optical Networks

We demonstrate diffractive optical networks that are trained with deep learning to engineer input terahertz pulses into desired temporal waveforms using passive diffractive surfaces that control the spectral phase and amplitude of the output pulse.
Development of the InBan_CIDO Ontology by Reusing the Concepts Along with Detecting Overlapping Information

The COVID-19 pandemic is a global emergency that badly impacted the economies of various countries. COVID-19 hits India when the growth rate of the country was at the lowest in the last ten years. To semantically analyze the impact of this pandemic on the economy, it is curial to have an ontology. CIDO ontology is a well-standardized ontology that is specially designed to assess the impact of coronavirus disease and utilize its results for future decision forecasting for the government, industry experts, and professionals in the field of various domains like research, medical advancement, technical innovative adoptions, and so on. However, this ontology does not analyze the impact of the COVID-19 pandemic on the Indian banking sector. On the other side, COVID-19-IBO ontology has been developed to analyze the impact of the COVID-19 pandemic on the Indian banking sector, but this ontology does not reflect complete information of COVID-19 data. Resultantly, users cannot get all the relevant information about the COVID-19 and its impact on the Indian economy. This article aims to extend the CIDO ontology to show the impact of COVID-19 on the Indian economy sector by reusing the concepts from other data sources. We also provide a simplified schema matching approach that detects the overlapping information among the ontologies. The experimental analysis proves that the proposed approach has reasonable results.
Development of a Mobile Robot That Plays Tag with Touch-and-Away Behavior Using a Laser Range Finder

The development of robots that play with humans is a challenging topic for robotics. We are developing a robot that plays tag with human players. To realize such a robot, it needs to observe the players and obstacles around it, chase a target player, and touch the player without collision. To achieve this task, we propose two methods. The first one is the player tracking method, by which the robot moves towards a virtual circle surrounding the target player. We used a laser range finder (LRF) as a sensor for player tracking. The second one is a motion control method after approaching the player. Here, the robot moves away from the player by moving towards the opposite side to the player. We conducted a simulation experiment and an experiment using a real robot. Both experiments proved that with the proposed tracking method, the robot properly chased the player and moved away from the player without collision. The contribution of this paper is the development of a robot control method to approach a human and then move away safely.
Understanding incentives for cybersecurity investments: Development and application of a typology

Digitalisation has tremendous benefits while simultaneously elevating cybersecurity to a prominent theme in modern societies. All businesses and organisations need invest in and manage their cybersecurity measures to ensure the continuation of their processes. However, the academic understanding regarding different incentives for these investments are fragmented throughout many different studies and a clear overview of these types of incentives for cybersecurity is lacking. This research aims to fill this deficiency by providing clarity on how incentives can be conceptualised, and what they mean in the context of investing and managing cybersecurity. This article provides a typology of cybersecurity incentives of organisations that can be used by scholars and professionals to understand the (lack of) adoption of cybersecurity measures. The typology is developed on the basis of a literature study encompassing different theoretical perspectives on incentives, and illustrated and further scrutinised with an empirical case about the adoption of secure e-mail standards. We present a typology of six categories of incentives that may explain why organisations are (not) willing to invest in cybersecurity measures: economic-, normative-, historic- and feasibility incentives, network externalities, and the presence of competing cybersecurity issues and solutions. This typology can serve as a starting point for future research to develop a (full) conceptual framework for identifying and understanding incentives for cybersecurity. Furthermore, cybersecurity professionals (e.g. Chief Information Security Officers) and policy makers can use this typology in their work to enhance the cybersecurity of organisations and society.
Artificial Intelligence in Music Education

In order to overcome the shortcomings of traditional music teaching methods, this paper proposes a music teaching method based on artificial intelligence. The new method combines the characteristics of traditional music education and artificial intelligence to improve the quality of music teaching. The experimental results show that artificial intelligence can effectively improve the quality of traditional music teaching and effectively promote the development of music education
Dataset for modulation classification and signal type classification for multi-task and single task learning

Wireless signal characterization is a growing area of research and an essential tool to enable spectrum monitoring, tactical signal recognition, spectrum management, signal authentication for secure communication, and so on. Recent years have witnessed several deep neural network models to perform single task signal characterization such as radio fingerprinting for emitter identification, automatic modulation classification, spectrum sharing, etc. However, with the emergence of 5G and the prospects of beyond 5G communication, there has been an increased deployment of edge devices that requires lightweight neural network models to perform signal characterization. To this end, a multi-task learning model that can perform multiple signal characterization tasks with a single neural network model has been proposed. However, due to the novel nature of multi-task learning as applied to signal characterization, there is a lack of a corresponding dataset with multiple labels for each waveform. In this paper, we openly share a synthetic wireless waveforms dataset suited for modulation recognition and wireless signal (protocol) classification tasks separately as well as jointly. The waveforms comprise radar and communication waveforms generated with GNU Radio to represent a heterogeneous wireless environment.
Towards Reducing Communication Gaps in Multicultural and Global Requirements Elicitation

This paper focuses on the collaborative aspects of requirements elicitation, in the context of software, systems and service development. The aim is to identify and understand challenges of requirements elicitation in general and in distributed environments. We focus on human, social, and cultural factors that have an impact on communication in the requirements elicitation process. More specifically we aim to i) unfold potential cultural impediments that hamper the requirements elicitation process; ii) highlight cultural factors that should be taken into account in the requirements elicitation process in order to avoid incomplete and inconsistent requirements; and iii) make recommendations for alleviating the problems. In this paper our first step is to report on the findings of a review of the literature regarding culture in RE. The results suggest that the cultural studies in the field of RE are insufficient and thus more empirical studies are required. Secondly, we look at current solutions that are being adopted to assist in improving the cultural aspect of the requirements elicitation process. In the following step we map the identified communication gaps to the SPI Manifesto Values revealing the manifestations of the problems and finally we prescribe a set of recommendations that could be exercised and fulfilled by actors in the requirements elicitation process in order for them to improve cultural considerations in the RE process. These recommendations address the shortcomings that were identified in the literature review and mapped the Values of the SPI Manifesto. The proposals regard technologies, platforms, methods, and frameworks that are readily available. A requirements elicitation process that adopts one or a number of these proposals can help alleviate the challenges invoked by stakeholders’ cultural diversity in the RE process, thus leading to systems development and deployment that much better reflects the requirements/needs of diverse stakeholders and users.
Blockchain Layer Zero: Characterizing the Bitcoin Network through Measurements, Models, and Simulations

In recent years, research has shown the networking layer’s significant influence on the scalability, security, and privacy of blockchain systems. Such large-scale networks however exhibit a degree of complexity that demands model-based simulations as real-world experiments are often not possible. In this work, we methodically characterize blockchain networks by reference to the paradigmatic Bitcoin peer-to-peer network, explore the state-of-the-art protocols, and emphasize this key design space. To this end, we conducted a longitudinal measurement study on the Bitcoin network, from which we extract a comprehensive network model and implement it as part of the bns network simulation framework. We validate the model in comparison to real-world measurements as well as to results from related work. Moreover, we experimentally show how network utilization and miners’ geographical location impact the block propagation characteristics.
A demonstration of NoDA

In this demo paper, we present a system prototype, called NoDA, that unifies access to NoSQL stores, by exposing a single interface to big data developers. This hides the heterogeneity of NoSQL stores, in terms of different query languages, non-standardized access, and different data models. NoDA comprises a layer positioned on top of NoSQL stores that defines a set of basic data access operators (filter, project, aggregate, etc.), implemented for different NoSQL engines. The provision of generic data access operators enables a declarative interface using SQL as query language. Furthermore, NoDA is extended to provide more complex operators, such as geospatial operators, which are only partially supported by NoSQL stores. We demonstrate NoDA by showcasing that the exact same query can be processed by different NoSQL stores, without any modification or transformation whatsoever.
Improving Multi-Speaker TTS Prosody Variance with a Residual Encoder and Normalizing Flows

Text-to-speech systems recently achieved almost indistinguishable quality from human speech. However, the prosody of those systems is generally flatter than natural speech, producing samples with low expressiveness. Disentanglement of speaker id and prosody is crucial in text-to-speech systems to improve on naturalness and produce more variable syntheses. This paper proposes a new neural text-to-speech model that approaches the disentanglement problem by conditioning a Tacotron2-like architecture on flow-normalized speaker embeddings, and by substituting the reference encoder with a new learned latent distribution responsible for modeling the intra-sentence variability due to the prosody. By removing the reference encoder dependency, the speaker-leakage problem typically happening in this kind of systems disappears, producing more distinctive syntheses at inference time. The new model achieves significantly higher prosody variance than the baseline in a set of quantitative prosody features, as well as higher speaker distinctiveness, without decreasing the speaker intelligibility. Finally, we observe that the normalized speaker embeddings enable much richer speaker interpolations, substantially improving the distinctiveness of the new interpolated speakers.
Efficient Distributed Source Coding of Fragmented Genomic Sequencing Data

In this paper we present a new compression scheme for genomic read data produced by modern sequencing technologies. In this setting, a reference genome similar to the one being sequenced is available only at the decoder, while the starting index of each read in this reference in unknown. The proposed scheme significantly reduces the encoding complexity relative to known reference-based compression schemes. The results include a code construction based on generalized concatenation coset codes, analysis of the decoding failure probability, and optimization of the scheme parameters for minimal compression rate.
Collaborative Filtering Technical Comparison in Implicit Data

Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set benchmark is crucial for the proper evaluation of collaborative filtering algorithms in order to draw a conclusion on the performance of the algorithms.