Reviewerly/exHarmony huggingface dataset:
On the Complexity of Decomposable Randomized Encodings, Or: How Friendly Can a Garbling-Friendly PRF Be?

Garbling schemes, also known as decomposable randomized encodings (DRE), have found many applications in cryptography. However, despite a large body of work on constructing such schemes, very little is known about their limitations.We initiate a systematic study of the DRE complexity of Boolean functions, obtaining the following main results: - Near-quadratic lower bounds. We use a classical lower bound technique of Neciporuk [Dokl. Akad. Nauk SSSR '66] to show an Ω(n²/log n) lower bound on the size of any DRE for many explicit Boolean functions. For some natural functions, we obtain a corresponding upper bound, thus settling their DRE complexity up to polylogarithmic factors. Prior to our work, no superlinear lower bounds were known, even for non-explicit functions. - Garbling-friendly PRFs. We show that any exponentially secure PRF has Ω(n²/log n) DRE size, and present a plausible candidate for a garbling-optimal PRF that nearly meets this bound. This candidate establishes a barrier for super-quadratic DRE lower bounds via natural proof techniques. In contrast, we show a candidate for a weak PRF with near-exponential security and linear DRE size. Our results establish several qualitative separations, including near-quadratic separations between computational and information-theoretic DRE size of Boolean functions, and between DRE size of weak vs. strong PRFs.
Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

The overall control system for an open-frame Remotely Operated Vehicle (ROV) is typically built from three subsystems: guidance, navigation and control (GNC). The control allocation plays a vital role in the control subsystem. Typically, open-frame underwater vehicles have p actuators (thrusters) for the motion in the horizontal plane, and the control allocation problem, in this case, is very complex and hard to visualise, because the normalised constrained control subset is a p-dimensional unit cube. The aim of this paper is to give a clear picture and a geometric interpretation of the problem and to introduce a hybrid method, based on the integration of a weighted pseudoinverse and the fixed-point method. The main idea of the hybrid method is visualised, and the deep geometric insight is provided using a “virtual” ROV in low-dimensional control spaces, including visualisation of the attainable command set, solution lines, control energy spheres and the role of pseudoinverse and fixed-point iterations. The same concepts are then extended to higher-dimensional cases, for open-frame ROV with four X-shaped (vectored) horizontal thrusters, which is one of the most common thruster configurations for commercial ROVs. The proposed hybrid method has been developed, integrated into a generic fault-tolerant ROV control system and evaluated in virtual and real-world environments off the west coast of Ireland using observation-class ROV Latis and work-class ROV Étaín.
Separating Two-Round Secure Computation From Oblivious Transfer.

We consider the question of minimizing the round complexity of protocols for secure multiparty computation (MPC) with security against an arbitrary number of semi-honest parties. Very recently, Garg and Srinivasan (Eurocrypt 2018) and Benhamouda and Lin (Eurocrypt 2018) constructed such 2-round MPC protocols from minimal assumptions. This was done by showing a round preserving reduction to the task of secure 2-party computation of the oblivious transfer functionality (OT). These constructions made a novel non-black-box use of the underlying OT protocol. The question remained whether this can be done by only making black-box use of 2-round OT. This is of theoretical and potentially also practical value as black-box use of primitives tends to lead to more efficient constructions.Our main result proves that such a black-box construction is impossible, namely that non-black-box use of OT is necessary. As a corollary, a similar separation holds when starting with any 2-party functionality other than OT. As a secondary contribution, we prove several additional results that further clarify the landscape of black-box MPC with minimal interaction. In particular, we complement the separation from 2-party functionalities by presenting a complete 4-party functionality, give evidence for the difficulty of ruling out a complete 3-party functionality and for the difficulty of ruling out black-box constructions of 3-round MPC from 2-round OT, and separate a relaxed non-compact variant of 2-party homomorphic secret sharing from 2-round OT.
High-quality versatile photonic sources for multiple quantum optical experiments

Entangled sources are important components for quantum information science and technology (QIST). The ability to generate high-quality entangled sources will determine the extent of progress in this field. Unlike previous schemes, a thin quasi-phase matching nonlinear crystal and a dense-wave-division-multiplexing device are used here to build high-quality versatile photonic sources with a simple configuration that can be used to perform Hong-Ou-Mandel interference, time-energy entanglement and multi-channel polarization entanglement experiments. The measurement results from various quantum optical experiments show the high quality of these photonic sources. These multi-functional photonic sources will be very useful in a variety of QIST applications.
Understanding the Statement on Standards for Forensic Services No. 1 and Its Implications for Practice, Curricula, and Research

ABSTRACT In July 2019, the AICPA issued the first authoritative professional standard on forensic services. The new standard articulates a novel paradigm for understanding what constitutes a forensic service, focusing on whether the service is for a litigation or investigation purpose, as defined. SSFS1 consolidates prior guidance into a single, clear statement and imposes some new restrictions, including a prohibition on the ultimate conclusion of fraud. It has important implications for practitioners, educators, and researchers alike, all with the goal of increasing the quality and consistency of forensic service engagements. Practitioners need to reassess the applicability of the standard and its impact on engagement acceptance and execution processes. Educators should feature SSFS1 prominently in their curricula, leveraging it for lessons on practice, ethics, and decision-making skills. Researchers have rich opportunities to contribute scholarly work to support future standard-setting and the public interest in this maturing area of the profession. JEL Classifications: K13; K41; M41; M42; M48. Data Availability: Data are generally available from the public sources cited in the text.
Localization System Supporting People with Cognitive Impairment and Their Caregivers

Localization systems are an important component of Active and Assisted Living (AAL) platforms supporting persons with cognitive impairments.The paper presents a positioning system being a part of the platform developed within the IONIS European project.The system's main function is providing the platform with data on user mobility and localization, which would be used to analyze his/her behavior and detect dementia wandering symptoms.An additional function of the system is localization of items, which are frequently misplaced by dementia sufferers.The paper includes a brief description of system's architecture, design of anchor nodes and tags and exchange of data between devices.both localization algorithms for user and item positioning are also presented.Exemplary results illustrating the system's capabilities are also included.
Multiple-image encryption method based on spatial angle multiplexing and double random phase encoding

An optical encryption method of multiple-image based on spatial angle multiplexing and double random phase encoding is proposed in this paper. In the encryption process, firstly the original images are modulated by random phase in Fresnel transform with different diffraction distances. Secondly, the modulated images are coherently superposed with reference beams which have different spatial angles and random phases, to generate interference fringes. Finally, the interference fringes from different directions are superposed to form a compound encrypted image. In the decryption process, the compound image is placed in a spatial filtering and Fresnel diffraction system, and the decrypted images are obtained after implementing the different phase keys’ demodulation and Fresnel diffraction with correct distance. This method encrypts multiple images into a single gray-scale image, which is easy to save and transmit. The double random phases are placed in object light and reference light respectively, which reduces the complexity of the encryption system and overcomes the difficulty of pixel-by-pixel alignment of random phase keys in traditional decryption experiment. At the same time, the multiplexing capacity of the proposed encryption system is analyzed, and the result shows that the system has sufficient encryption capacity. So the proposed method possesses the characteristics of high storage efficiency, simple calculation and strong anti-noise ability, and thus can encrypt multiple images simultaneously. In this paper, the encryption effect is evaluated by correlation coefficient, while the effectiveness and security are verified by simulation experiment.
Cameron-Liebler line classes in AG(3,q)

The study of Cameron-Liebler line classes in PG($3,q$) arose from classifying specific collineation subgroups of PG($3,q$). Recently, these line classes were considered in new settings. In this point of view, we will generalize the concept of Cameron-Liebler line classes to AG($3,q$). In this article we define Cameron-Liebler line classes using the constant intersection property towards line spreads. The interesting fact about this generalization is the link these line classes have with Cameron-Liebler line classes in PG($3,q$). Next to giving this link, we will also give some equivalent ways to consider Cameron-Liebler line classes in AG($3,q$), some classification results and an example based on the example found in [3] and [6].
Quantum semi-supervised kernel learning

Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the training dataset. Thus, quantum machine learning methods have the potential to facilitate learning using extremely large datasets. While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels. One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors. Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines. The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss. Through a theoretical study of the algorithm's computational complexity, we show that it maintains the same speedup as the fully-supervised Quantum LS-SVM.
An Evolutionary Computation Approach for Approximate Computing of PNN Hardware Circuits

Approximate computing is a way to help computer system greatly improve computing efficiency. Compared with traditional computer system's computing mode, the approximate computation of computers may be able to accomplish more tasks under the same resource consumption. In general, DSP hardware architecture requires using a large number of floating-point operations and multiplier, which will cost a large amount of hardware resources; using fixed-point arithmetic implemented in hardware enables the DSP algorithm to processing the constant multiplication simultaneously. However, on the other hand, this can affect the accuracy of the calculation results. Facing these hardware circuit design problems, this research attempt to realize the Probabilistic Neural Network (PNN) hardware architecture of approximate calculation with using genetic algorithm (GA). Considering hardware resource consumption and computing speed, we hope that by sacrificing the precision of operational results to reduce the hardware complexity of the PNN hardware circuit.
Profiling Social Media Users, a Content-Based Data Mining Technique for Twitter Users

Trends in Social Networking have brought millions under a single roof. The mode of information sharing by people around the globe has been occupied by chat, blogs, tweets and so on, using different social media sites. To profile the user with respect to the topic(s) discussed in social media, Profiling Social Media Users (PSMU) algorithm is proposed in this work. Using NER approach, the keywords in the posts are clustered into seven predefined clusters and further, unique clusters are formed based on synonymous of keywords in posts using WordNet approach. Users are profiled based on having highest number of keywords in respective clusters. Experiments conducted on real world data set from Twitter Social Media show unique clusters being formed by PSMU and profiles the Twitter Users accurately by achieving 97.53 percent accuracy.
Dual Vigilance Hypersphere Adaptive Resonance Theory

The internal representation of the categories in Adaptive Resonance Theory (ART) neural networks can greatly affect the quality and compactness of the discovered clusters. Dual vigilance thresholds have been shown to yield a significant improvement in Fuzzy ART performance and allow it to retrieve arbitrarily shaped clusters while maintaining the important advantage of a simple architecture and implementation. In this study, we examine the use of Hypersphere ART within the same dual vigilance architecture, thereby presenting the Dual Vigilance Hypersphere ART (DVHA). We conduct an extensive comparison between 6 different ART-based approaches across a set of 30 benchmark datasets, using two different input ordering methods and 30 repeated runs. We find that DVHA ranks better than Dual Vigilance Fuzzy ART (DVFA) on average for many datasets, both in terms of performance and network compactness. Furthermore, although another multi-category-based architecture showed statistically superior results when the inputs are shuffled, we found no statistical difference in performance when the input was pre-processed using the visual assessment of cluster tendency (VAT), while generally being much simpler to implement and less computationally demanding. These findings make DVHA a viable alternative to its Fuzzy ART counterpart, and a simpler alternative to the other studied multi-category-based approaches in cases where resources are limited, such as embedded and hardware-based applications, provided that the input can be preprocessed using VAT.
A Fuzzy Approach for Cost and Time Optimization in Agile Software Development

Cost and time prediction is very crucial for software development. For many years conventional techniques to determine them were followed, but nowadays the software companies are moving toward adopting agile paradigm. It is due to its various characteristics such as iterative development, rapid delivery, and reduced risk. This work proposes the role of fuzzy logic in agile software cost and time optimization. Here, both fuzzy type-1 and type-2 are evaluated on agile dataset.
Electronic taxation and cybercrimes in Nigeria, Kenya and South Africa: Lessons from Europe and the United States of America

Advancement in Information and Communication Technology (ICT) over the years has changed the way people now live and conduct their businesses. Consequently, tax administrators in line with global trends are shifting from manual processes to electronic tax systems. Although the adoption of ICT has apparent advantages, it does not come without some potential challenges, prominent among which are the activities of cybercriminals. This paper seeks to identify the ways electronic tax systems can be protected and sustained in some African countries, drawing from the lessons the experiences of the United States of America (US) and some countries in Europe present.
Conceptual Game Expansion

Automated game design is the problem of automatically producing games through computational processes. Traditionally, these methods have relied on the authoring of search spaces by a designer, defining the space of all possible games for the system to author. In this paper, we instead learn representations of existing games from gameplay video and use these to approximate a search space of novel games. In a human subject study we demonstrate that these novel games are indistinguishable from human games in terms of challenge, and that one of the novel games was equivalent to one of the human games in terms of fun, frustration, and likeability.
ACPO principles for digital evidence: Time for an update?

Despite remaining largely unchanged for over 10 years, the Association of Chief Police Officers’s [1] Good Practice Guides for Digital Evidence and their four governing principles for evidence handling are amongst some of the most cited pieces of digital forensic best practice advice. However, given the pace of change in both technology and the field of digital forensics, this work debates whether it may be time to evaluate whether these principles remain wholly valid given the current forensic analysis landscape and their lack of updating or periodic evaluation. A discussion of the existing four ACPO principles is provided followed by an offering of eight new revised principles as a means of acknowledging the current challenges faced by practitioners in this field. It is hoped that this piece will spark a debate surrounding the principles we so frequently acknowledge as a mark of quality assurance in our investigations, and be a catalyst for evaluative considerations in this area.
Deception, Robustness and Trust in Big Data Fueled Deep Learning Systems

We are entering an exciting era where human intelligence is being enhanced by machine intelligence through big data fueled artificial intelligence (AI) and machine learning (ML). However, recent work shows that DNN models trained privately are vulnerable to adversarial inputs. Such adversarial inputs inject small amount of perturbations to the input data to fool machine learning models to misbehave, turning a deep neural network against itself. As new defense methods are proposed, more sophisticated attack algorithms are surfaced. This arms race has been ongoing since the rise of adversarial machine learning. This keynote provides a comprehensive analysis and characterization of the most representative attacks and their defenses. As more and more mission critical systems are incorporating machine learning and AI as an essential component in their real-world big data applications and their big data service provisioning platforms or products, understanding and ensuring the verifiable robustness of deep learning becomes a pressing challenge in the presence of adversarial attacks. This includes (1) the development of formal metrics to quantitatively evaluate and measure the robustness of a DNN prediction with respect of intentional and unintentional artifacts and deceptions, (2) the comprehensive understanding of the blind spots and the invariants in the DNN trained models and the DNN training process, and (3) the statistical measurement of trust and distrust that we can place on a deep learning algorithm to perform reliably and truthfully. In this keynote talk, I will use empirical analysis and evaluation of our cross-layer strategic teaming defense framework and techniques to illustrate the feasibility of ensuring robust deep learning.
Mapping the State of Security Standards Mappings

Companies often have to comply with more than one security standard and refine parts of security standards to apply to their domain and specific security goals. To understand which requirements different security standards stipulate, a systematic overview or mapping of the relevant natural language security standards is necessary. Creating such standards mappings is a difficult task; to discover which methodologies and tools researchers and practitioners propose and use to map security standards, we conducted a systematic literature review. We identified 44 resources published between 2004 and 2018 using ACM Digital Library, IEEEXplore, SpringerLink, ScienceDirect, dblp and additional grey literature sources. We found that research focuses either on manual methods or on security ontologies to create security standards mappings. We also observed an increase in scientific publications over the investigated timespan which we attribute to the ISO 27001 standard update in 2013 and the EU GDPR coming into effect in 2018.
The digital smash product

&lt;p style='text-indent:20px;'&gt;In this paper, we construct the smash product from the digital viewpoint and prove some its properties such as associativity, distributivity, and commutativity. Moreover, we present the digital suspension and the digital cone for an arbitrary digital image and give some examples of these new concepts.&lt;/p&gt;
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks

It has been demonstrated that very simple attacks can fool highly-sophisticated neural network architectures. In particular, so-called adversarial examples, constructed from perturbations of input data that are small or imperceptible to humans but lead to different predictions, may lead to an enormous risk in certain critical applications. In light of this, there has been a great deal of work on developing adversarial training strategies to improve model robustness. These training strategies are very expensive, in both human and computational time. To complement these approaches, we propose a very simple and inexpensive strategy which can be used to ``retrofit'' a previously-trained network to improve its resilience to adversarial attacks. More concretely, we propose a new activation function---the JumpReLU---which, when used in place of a ReLU in an already-trained model, leads to a trade-off between predictive accuracy and robustness. This trade-off is controlled by the jump size, a hyper-parameter which can be tuned during the validation stage. Our empirical results demonstrate that this increases model robustness, protecting against adversarial attacks with substantially increased levels of perturbations. This is accomplished simply by retrofitting existing networks with our JumpReLU activation function, without the need for retraining the model. Additionally, we demonstrate that adversarially trained (robust) models can greatly benefit from retrofitting.
Analytic and directional wavelet packets

The paper presents a versatile library of analytic and quasi-analytic complex-valued wavelet packets (WPs) which originate from discrete splines of arbitrary orders. The real parts of quasi-analytic WPs are the regular spline-based orthonormal WPs designed in [1]. The imaginary parts are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Tensor products of 1D quasi-analytic WPs provide a diversity of 2D WPs oriented in multiple directions. For example, the set of fourth-level WPs comprise 62 different directions. The designed computational scheme enables extremely fast and easy implementation of the WP transforms.