Reviewerly/exHarmony huggingface dataset:
Indistinguishability Obfuscation from Simple-to-State Hard Problems: New Assumptions, New Techniques, and Simplification

In this work, we study the question of what set of simple-to-state assumptions suffice for constructing functional encryption and indistinguishability obfuscation ( $$i\mathcal {O}$$ ), supporting all functions describable by polynomial-size circuits. Our work improves over the state-of-the-art work of Jain, Lin, Matt, and Sahai (Eurocrypt 2019) in multiple dimensions. New Assumption: Previous to our work, all constructions of $$i\mathcal {O}$$ from simple assumptions required novel pseudorandomness generators involving LWE samples and constant-degree polynomials over the integers, evaluated on the error of the LWE samples. In contrast, Boolean pseudorandom generators (PRGs) computable by constant-degree polynomials have been extensively studied since the work of Goldreich (2000). (Goldreich and follow-up works study Boolean pseudorandom generators with constant-locality, which can be computed by constant-degree polynomials.) We show how to replace the novel pseudorandom objects over the integers used in previous works, with appropriate Boolean pseudorandom generators with sufficient stretch, when combined with LWE with binary error over suitable parameters. Both binary error LWE and constant degree Goldreich PRGs have been a subject of extensive cryptanalysis since much before our work and thus we back the plausibility of our assumption with security against algorithms studied in context of cryptanalysis of these objects. New Techniques: we introduce a number of new techniques: – We show how to build partially-hiding public-key functional encryption, supporting degree-2 functions in the secret part of the message, and arithmetic $$\mathsf {NC^1}$$ functions over the public part of the message, assuming only standard assumptions over asymmetric pairing groups. – We construct single-ciphertext secret-key functional encryption for all circuits with linear key generation, assuming only the LWE assumption. Simplification: Unlike prior works, our new techniques furthermore let us construct public-key functional encryption for polynomial-sized circuits directly (without invoking any bootstrapping theorem, nor transformation from secret-key to public key FE), and based only on the polynomial hardness of underlying assumptions. The functional encryption scheme satisfies a strong notion of efficiency where the size of the ciphertext grows only sublinearly in the output size of the circuit and not its size. Finally, assuming that the underlying assumptions are subexponentially hard, we can bootstrap this construction to achieve $$i\mathcal {O}$$ .
The Role of Knowledge Management Processes for Enhancing and Supporting Innovative Organizations: A Systematic Review

The rapidly advancing technology and the globalizing market have espoused the notion of knowledge management (KM) as an appropriate plan in the highly competitive commercial contexts. The importance of knowledge is evident in multidimensional society and the corporate domain. The use of acquaintance and information has currently increased in every type of business to simplify all the operations and improve the decision-making procedure. As such, the KM systems are among the fastest developing parts of the corporate sector. Likewise, the innovative competency offers mechanisms and unique insights that practitioners should consider in building up a creative business to raise performance. The business environment has dramatically transformed, and thereby, forcing companies to innovate in the ways they function and learn other new concepts. The present research reviews and sheds the light on innovation-related KM processes to provide a thorough assessment of 50 research articles published in peer-reviewed journals. The main findings of this study indicate that multiple KM processes can play an important role in an innovative organization. However, the most essential process for innovation was found to be knowledge sharing. Besides, KM was found to be involved in all of the innovation types.
A Comparative Study of Outfit Recommendation Methods with a Focus on Attention-based Fusion

In recent years, deep learning-based recommender systems have received increasing attention, as deep neural networks can detect important product features in images and text descriptions and capture them in semantic vector representations of items. This is especially relevant for outfit recommendation, since a variety of fashion product features play a role in creating outfits. This work is a comparative study of fusion methods for outfit recommendation that combine relevant product features extracted from visual and textual data in semantic, multimodal item representations. We compare traditional fusion methods with attention-based fusion methods, which are designed to focus on the fine-grained product features of items. We evaluate the fusion methods on four benchmark datasets for outfit recommendation and provide insights into the importance of the multimodality and granularity of the fashion item representations. We find that the visual and textual item data not only share product features but also contain complementary product features for the outfit recommendation task, confirming the need to effectively combine them into multimodal item representations. Furthermore, we show that the average performance of attention-based fusion methods surpasses the average performance of traditional fusion methods on three out of the four benchmark datasets, demonstrating the ability of attention to learn relevant correlations among fine-grained fashion attributes.
Memorable Maps: A Framework for Re-Defining Places in Visual Place Recognition

This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition. This framework draws inspiration from human-memorability, utilizes the traditional image entropy concept and computes the static content in an image; thereby presenting a tri-folded criteria to assess the ‘memorability’ of an image for visual place recognition. A dataset namely ‘ESSEX3IN1’ is created, composed of highly confusing images from indoor, outdoor and natural scenes for analysis. When used in conjunction with state-of-the-art visual place recognition methods, the proposed framework provides significant performance boost to these techniques, as evidenced by results on ESSEX3IN1 and other public datasets.
Extreme learning machine and swarm-based support vector regression methods for predicting crystal lattice parameters of pseudo-cubic/cubic perovskites

Lattice parameters of perovskite compounds play crucial roles in engineering of buffer layers and substrates for heteroepitaxial films. As a result, predictive models that can effectively estimate these lattice parameters are highly desired. Therefore, this work presents elegant methods of predicting lattice parameters of pseudo-cubic/cubic perovskite through the development of the extreme learning machine (ELM) based model and hybridization of the particle swarm optimization (PSO) technique with the support vector regression algorithm (SVRA). The generalization and predictive strengths of the proposed SVRA-PSO and ELM-based models are compared with existing methods such as the Ubic model and the recently developed Sidey model on the basis of root mean square error (RMSE), mean absolute error, mean absolute percentage error (MAPE), and correlation coefficient. The developed SVRA-PSO model performs better than the ELM-based model, the Ubic model, and the Sidey model, with performance improvement of 20.99%, 29.29%, and 33.39%, respectively, on the basis of MAPE. Similarly, the SVRA-PSO, respectively, attains performance improvement of 24.74%, 34%, and 37.89% on the basis of RMSE. Furthermore, the developed ELM-based model outperforms the Ubic and Sidey models with performance improvement of 15.70% and 10.50%, respectively, on the basis of MAPE and percentage enhancement of 17.48% and 12.31% when compared on the basis of RMSE. Although the SVRA-PSO model has the best performance of all the compared models, the developed ELM-based model possesses the advantage of easy implementation in addition to its moderate performance.
Dropout as an Implicit Gating Mechanism For Continual Learning

In recent years, neural networks have demonstrated an outstanding ability to achieve complex learning tasks across various domains. However, they suffer from the "catastrophic forgetting" problem when they face a sequence of learning tasks, where they forget the old ones as they learn new tasks. This problem is also highly related to the "stability-plasticity dilemma". The more plastic the network, the easier it can learn new tasks, but the faster it also forgets previous ones. Conversely, a stable network cannot learn new tasks as fast as a very plastic network. However, it is more reliable to preserve the knowledge it has learned from the previous tasks. Several solutions have been proposed to overcome the forgetting problem by making the neural network parameters more stable, and some of them have mentioned the significance of dropout in continual learning. However, their relationship has not been sufficiently studied yet. In this paper, we investigate this relationship and show that a stable network with dropout learns a gating mechanism such that for different tasks, different paths of the network are active. Our experiments show that the stability achieved by this implicit gating plays a very critical role in leading to performance comparable to or better than other involved continual learning algorithms to overcome catastrophic forgetting. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Crafting Research Products through Digital Machine Embroidery

Wearables combine practical and conceptual challenges related to electronics, clothing and interaction design. Research through design in this area is commonly done iteratively through prototypes of increasing levels of fidelity, often relying on manual fabrication. However, manual fabrication presents challenges when comparing prototypes due to their varying levels of realization and the inaccuracy of reproductions. We discuss how using digital machine embroidery, combined with chemical embroidery technique, supports fabricating consistent high-fidelity prototypes for soft wearables in the form of research products. Our approach involves creating the textile substrate together with integrating and embedding electronic components through a unified process whilst keeping high control over alterations between prototypes. We illustrate this approach through the design process of the Smart Sock, a sensorized sock for rehabilitation. We detail the challenges behind our process and reflect on the opportunities emerging from using digital machine and chemical embroidery techniques combined to craft research products.
Unsupervised Temporal Consistency Metric for Video Segmentation in Highly-Automated Driving

Commonly used metrics to evaluate semantic segmentation such as mean intersection over union (mIoU) do not incorporate temporal consistency. A straightforward extension of existing metrics towards evaluating the consistency of segmentation of video sequences does not exist, since labelled videos are rare and very expensive to obtain. For safety-critical applications such as highly automated driving, there is, however, a need for a metric that measures such temporal consistency of video segmentation networks to possibly support safety requirements. In this paper, (a) we introduce a metric which does not require segmentation labels for measuring the stability of the predictions of segmentation networks over a series of images; (b) we perform an in-depth analysis of the proposed metric and observe strong correlations to the supervised mIoU metric; (c) we perform an evaluation of five state-of-the-art networks for semantic segmentation of varying complexities and architectures evaluated on two public datasets, namely, Cityscapes and CamVid. Finally, we perform timing evaluations and propose the use of the metric as either an online observer for identification of possibly unstable segmentation predictions, or as an offline method to evaluate or to improve semantic segmentation networks, e.g., by selecting additional training data with critical temporal consistency.
Review of Clustering Techniques in Control System

Data clustering is an important tool in data mining, that helps to retrieve useful data from large amount of available data. In this digital era data is available in abundance, but finding useful data has become a challenging task. For this, data clustering is an effective and common approach where we can group data by seeing some pattern or inherent data similarity in one group. Clustering is an unsupervised learning method of linearly separable and nonlinearly separable clusters widely used for different nature of application [1]. Data clustering finds application in classification of patterns in different areas such as artificial intelligence, summarization, learning, segmentation, speech recognition, pattern recognition, image segmentation, biology, marketing, data mining, modelling and system identification etc [5][24][25]. No one clustering technique can be said as best or better than other, because different clustering algorithms co-exists and are application specific. This paper majorly emphasises on critical review of clustering algorithms used in control systems, but a brief overview is also given about all major algorithms.
BSPR: Basket-sensitive personalized ranking for product recommendation

Product recommendation has played an important role in improving user experiences and obtaining more profits. To optimize recommendation models, pairwise learning has become a mainstream method for modeling user preferences from implicit feedback. Nevertheless, most existing pairwise methods optimize the relative order among products from only the user perspective. In fact, the purchase decision making of a given user depends on not only individual taste, but also the complementary relationships between the recommended products and the products in his/her historical basket. We argue that it is challenging to uncover meaningful user and product representations by only utilizing the user-side pairwise ranking. Towards this end, we propose a novel probabilistic pairwise method named BSPR, short for basket-sensitive personalized ranking, which solves both user- and product-side pairwise ranking problems in a unified manner. Specifically, BSPR discovers mutual correlations among users and products by exploiting co-pairwise ranking, alleviating the inherent flaw in existing pairwise methods. Considering that the negative sampler is one of the key components for pairwise learning, we devise a position-aware sampling strategy for the proposed method. To solve the optimization problem in BSPR, we further design an alternative optimization algorithm to efficiently learn the model parameters. Extensive experiments on multiple real-world datasets demonstrate significant improvements of our method over a series of state-of-the-art methods. Our implementation of BSPR is publicly available at:https://github.com/wubinzzu/BSPR.
A New Family of APN Quadrinomials

The binomial B(x) = x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> +βx <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">36</sup> (where β is primitive in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 2) over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 10 is the first known example of an Almost Perfect Nonlinear (APN) function that is not CCZ-equivalent to a power function, and has remained unclassified into any infinite family of APN functions since its discovery in 2006. We generalize this binomial to an infinite family of APN quadrinomials of the form x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> +a(x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2i+1</sup> )2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> +bx <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3·2m</sup> +c(x2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i+m+2m</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> from which B(x) can be obtained by setting a = β, b = c = 0, i = 3, k = 2. We show that for any dimension n = 2m with m odd and 3 + m,setting(a, b, c)=(β, β <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 1) and i =m -2 or i = (m - 2) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> mod n yields an APN function, and verify that for n = 10 the quadrinomials obtained in this way for i = m - 2 and i = (m - 2) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> mod n are CCZ-inequivalent to each other, to B(x), and to any other known APN function over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 10.
Car Damage Detection and Classification

Nowadays, the proliferation of automobile industries is directly related to the increasing number of car incidents. So, insurance companies are facing many simultaneous claims and solving claims leakage. The sense of Artificial Intelligence (AI) based on machine learning and deep learning algorithms can help to solve these kinds of problem for insurance industries. In this paper, we apply deep learning-based algorithms, VGG16 and VGG19, for car damage detection and assessment in real-world datasets. The algorithms detect the damaged part of a car and assess its location and then its severity. Initially, we discover the effect of domain-specific pre-trained CNN models, which are trained on an ImageNet dataset, and followed by fine-tuning, because some of the categories can be fine-granular to get our specific tasks. Then we apply transfer learning in pre-trained VGG models and use some techniques to improve the accuracy of our system. We achieve the accuracy of 95.22% of VGG19 and 94.56% of VGG16 in the damaged detection, the accuracy of 76.48% of VGG19 and 74.39% of VGG16 in damage localization, the accuracy of 58.48% of VGG19 and 54.8% of VGG16 in damage severity with the combination of transfer learning and L2 regularization. From their results, the performance of VGG19 is better than VGG16. After analyzing and implementing our models, we find out that the results of using transfer learning and L2 regularization can work better than those of fine-tuning.
An Internet of Things Approach to Contact Tracing—The BubbleBox System

The COVID-19 pandemic exploded at the beginning of 2020, with over four million cases in five months, overwhelming the healthcare sector. Several national governments decided to adopt containment measures, such as lockdowns, social distancing, and quarantine. Among these measures, contact tracing can contribute in bringing under control the outbreak, as quickly identifying contacts to isolate suspected cases can limit the number of infected people. In this paper we present BubbleBox, a system relying on a dedicated device to perform contact tracing. BubbleBox integrates Internet of Things and software technologies into different components to achieve its goal—providing a tool to quickly react to further outbreaks, by allowing health operators to rapidly reach and test possible infected people. This paper describes the BubbleBox architecture, presents its prototype implementation, and discusses its pros and cons, also dealing with privacy concerns.
Victimization in Cyberspace: Is It How Long We Spend Online, What We Do Online, or What We Post Online?

Prior research on cybercrime victimization has generally emphasized the linkage between the frequency or actual length of time individuals spend online engaging in certain activities and the risk of being victimized in cyberspace but has paid much less attention to what persons actually share or post online that increases the risk of online victimization. To address this gap, we appeal to the integrated lifestyle–routine activities theory in order to examine the relationships between the length of time one spends online (online frequency), specific activities or tasks one engages in while online (online activity), specific types of information one shares online (online posting), and seven specific forms of cybercrime victimization using a convenience sample of students. Results showed that one online frequency variable (internet hours), six online activity variables (banking, reading news, shopping, planning travel, socializing, and communicating with stranger), and three online posting variables (phone number, home address, and other info) were significantly related to five of the seven forms of cybercrime victimization (computer virus, harassment by nonstranger unwanted porn, sex solicitation, and phishing). Implications for our findings and directions for future research are discussed.
Cloud Computing Tools: Inside Views and Analysis

Cloud computing tools can be segregated into various categories as indicated by their highlights. In this area, we have made an extensive investigation with various classes on the basis of the following parameters such as broadening, correlation and classification. In this paper, we have taken some of the important open-source Cloud tools for comparative study for their better utility. It is very much useful if we can optimize codes in subtleties, growing new calculations for all the users involved in various processes and its design. The five open-source Cloud computing tools, more specifically CloudSim, Cloud Analyst, Cloud Reports, Cloudsched and Green Cloud are illustrative of many related test systems differ from each other on the parameters like architecture design, modeling elements, simulation process, performance metrics and scalability. These tools have regular highlights by their commercial vendors, particularly in design, displaying components, and reproduction process. These are some of the important characteristics, which can be utilized to study the utilities of these tools, for example: concentrating on different layers and with several execution measurements.
Online Learning During COVID-19

The COVID-19 pandemic has forced educational institutes worldwide to resort to an "online only" mode of teaching delivery. As a consequence, throughout the globe there has been an increasing trend among the students to use different videoconferencing applications for the purpose of learning online. However, the multimedia quality provided by these different applications provides the key to their success i.e. whether or not the students will be willing to use those for learning online. Consequently, three popular applications (Zoom, Microsoft Teams, and Cisco Webex) are taken up in this work for the purpose of multimedia quality evaluation by using an objective based approach. Results from both the models are in close agreement with each other. Microsoft Teams provides the least experience, whereas those from the others vary depending upon the objective models used. The results obtained are further verified by conducting relevant hypotheses tests.