Reviewerly/exHarmony huggingface dataset:
Interpolation Cryptanalysis of Unbalanced Feistel Networks with Low Degree Round Functions

In recent years a new type of block ciphers and hash functions over a (large) field, such as MiMC and GMiMC, have been designed. Their security, particularly over a prime field, is mainly determined by algebraic cryptanalysis techniques, such as Gröbner basis and interpolation attacks. In SAC 2019, Li and Preneel presented low memory interpolation attack against the MiMC and Feistel-MiMC designs. In this work we answer the open question posed in their work and show that low memory interpolation attacks can be extended to unbalanced Feistel networks (UFN) with low degree functions, and in particular to the GMiMC design. Our attack applies to UFNs with expanding and contracting round functions keyed either via identical (univariate) or distinct round keys (multivariate). Since interpolation attacks do not necessarily yield the best possible attacks over a binary extension field, we focus our analysis on prime fields $$\mathbb {F}_{\!p}$$ . Our next contribution is to develop an improved technique for a more efficient key recovery against UFNs with expanding round function. We show that the final key recovery step can be reduced not only to the gcd but also to the root finding problem. Despite its higher theoretical complexity, we show that our approach has a particularly interesting application on Sponge hash functions based on UFNs, such as GMiMCHash. We illustrate for the first time how our root finding technique can be used to find collision, second preimage and preimage attacks on (reduced round) members of the GMiMCHash family. In addition, we support our theoretical analysis with small-scale experimental results.
Implementasi E-Learning Dalam Pembelajaran Bahasa Inggris Di Pendidikan Tinggi Pariwisata Di Bali Selama Pandemi Covid-19

Covid-19 has been declared as a global pandemic by WHO. It affects all aspects of humans’ life, including their education. Teaching and learning processes during the pandemic of Covid-19 are done online or by implementing e-learning. This is to avoid the contact among people. This study aims to find out the media used to do e-learning, the consideration in picking the media, as well as the advantages and disadvantages found during the implementation of e-learning. This research involved 6 English lecturers teaching in some tourism vocational universities in Bali. Data of the research were gained through online questionnaires. Then the data were analysed by finding the percentages as well as describing them. It is found that (1) Google Classrom is used by most od the respondents to do e-learning, then followed by Zoom, Edmodo, WhatsApp, and YouTube, (2) accessibility, communication feature, media supported, the provided assessment types, design, and the pricing and budget are the consideration for respondents in picking the e-learning media, (3) the inequality of internet access is the biggest challenge faced by the lecturers in implementing e-learning, but they found that it provides a flexibility in terms of time and spaces. Keywords: Covid-19 Pandemic, E-learning, English Learning
Revisiting the equality conditions of the data-processing inequality for the sandwiched Rényi divergence

We provide a transparent, simple, and unified treatment of recent results on the equality conditions for the data-processing inequality of the sandwiched quantum Rényi divergence, including the statement that the equality in the data-processing implies recoverability via the Petz recovery map for the full range of the Rényi parameter α recently proven by Jenčová [J. Phys. A: Math. Theor. 50, 085303 (2017)]. We also obtain a new set of equality conditions, generalizing a previous result by Leditzky et al. [Lett. Math. Phys. 107, 61 (2017)].
VALUE at RISK (VaR) DAN CONDITIONAL VALUE at RISK (CVaR) DALAM PEMBENTUKAN PORTOFOLIO BIVARIAT MENGGUNAKAN COPULA GUMBEL

One way to minimize risk in investing is to form of portfolio by combining several stocks.Value at Risk (VaR) is a method for estimating risk but has a weakness that is VaR is incoherent because it does not have the subadditivity. To overcome the weakness of VaR, Conditional Value at Risk (CVaR) can use. Stock data is generally volatile, so ARIMA-GARCH is used to model it. The selection of ARIMA models on R software can be automatically using the auto.arima() function. Then Copula Gumbel is a method for modeling joint distribution and flexible because it does not require the assumption of normality and has the best sensitivity to high risk so that it is suitable for use in stock data.The first step in this research is to modeling Copula Gumbel-GARCH with the aim to calculate VaR and CVaR on the portfolio of PT Bank Mandiri Tbk (BMRI) and PT Indo Tambangraya Megah Tbk (ITMG). At the confidence level 99%, 95%, and 90% obtained the VaR results sequentially amounted to 3.977073%; 2.546167%; and 1.837288% and the CVaR results sequentially amounted to 4.761437%; 3.457014%; and 2.779182%. The worst condition is a loss with VaR and it is still possible if a worse condition occurs is a loss with CVaR so that investors can be more aware of the biggest loss that will be suffered.Keywords: Value at Risk, Conditional Value at Risk, Auto ARIMA, Copula Gumbel.
Cryptocurrency Is Garbage. So Is Blockchain.

The entire space cryptocurrency/blockchain space is dominated by false claims, conspiracy theories, muddled thinking, and outright fraud of many kinds. It is remarkable how much academic, journalistic and popular writing on blockchain accepts at face value dogma that any dispassionate investigation shows to be false, This paper consists mainly of two lists: falsehoods that nobody who is interested in the world as it really is should ever repeat, at least not without heavy qualification; the second a list of truths and rules of thumb about cryptocurrency and blockchain that have been demonstrated repeatedly (often for many years) but escape notice far too often. Each item in the list is accompanied with some, but only a small subset, of the evidence available to support it.
A descriptive study for the strategic role of human resource flexibility as a full model of organizational ambidexterity in Isfahan Payame Noor University

The behavior, practice, and skills of human resources can be effective in advancing the organizational goals of universities. Therefore, the strategic role that staff might play in the university to achieve its goals and maintain its position is an issue worth being addressed. The purpose of the article was to examine how human resource flexibility in the university facilitates organizational ambidexterity and performance. The research hypotheses were tested by forming a structural equation of the research variables using the partial least squares method. The studied population consisted of Payame Noor University of Isfahan staff members. The results of this research showed that flexibility of human resources is positively related to organizational ambidexterity. This relationship was observed in each of the three components of Human Resource (HR) flexibility including behavioral, skill, and practical flexibility. However, the results also indicated no significant effect for organizational ambidexterity on performance. Furthermore, no positive effect for human resource flexibility on university performance was observed whether directly or mediated by organizational ambidexterity. This study shows that the goals and tasks of Payame Noor University will only be achieved through the flexibility of its human resources in its three aspects.
Characterization and ENOB Analysis of a Reconfigurable Linear Optical Processor

The characterization of a broadband low-loss 4 × 4 MZI-based reconfigurable linear optical processor is reported. The impact of MZI extinction ratio on the effective number of bits (ENOB) at the device output is also investigated.
Transforming Lindblad Equations into Systems of Real-Valued Linear Equations: Performance Optimization and Parallelization of an Algorithm

With their constantly increasing peak performance and memory capacity, modern supercomputers offer new perspectives on numerical studies of open many-body quantum systems. These systems are often modeled by using Markovian quantum master equations describing the evolution of the system density operators. In this paper, we address master equations of the Lindblad form, which are a popular theoretical tools in quantum optics, cavity quantum electrodynamics, and optomechanics. By using the generalized Gell–Mann matrices as a basis, any Lindblad equation can be transformed into a system of ordinary differential equations with real coefficients. Recently, we presented an implementation of the transformation with the computational complexity, scaling as O(N5logN) for dense Lindbaldians and O(N3logN) for sparse ones. However, infeasible memory costs remains a serious obstacle on the way to large models. Here, we present a parallel cluster-based implementation of the algorithm and demonstrate that it allows us to integrate a sparse Lindbladian model of the dimension N=2000 and a dense random Lindbladian model of the dimension N=200 by using 25 nodes with 64 GB RAM per node.
CYK Parsing over Distributed Representations

Parsing is a key task in computer science, with applications in compilers, natural language processing, syntactic pattern matching, and formal language theory. With the recent development of deep learning techniques, several artificial intelligence applications, especially in natural language processing, have combined traditional parsing methods with neural networks to drive the search in the parsing space, resulting in hybrid architectures using both symbolic and distributed representations. In this article, we show that existing symbolic parsing algorithms for context-free languages can cross the border and be entirely formulated over distributed representations. To this end, we introduce a version of the traditional Cocke–Younger–Kasami (CYK) algorithm, called distributed (D)-CYK, which is entirely defined over distributed representations. D-CYK uses matrix multiplication on real number matrices of a size independent of the length of the input string. These operations are compatible with recurrent neural networks. Preliminary experiments show that D-CYK approximates the original CYK algorithm. By showing that CYK can be entirely performed on distributed representations, we open the way to the definition of recurrent layer neural networks that can process general context-free languages.
Neural Novelty — How Machine Learning Does Interactive Generative Literature

Every day, machine learning (ML) and artificial intelligence (AI) embeds itself further into domestic and industrial technologies. Interaction de- signers have historically struggled to engage dire ...
Supercontinuum laser illumination for NIR reflectance spectroscopy of minerals and speckle reduction

We propose a method to significantly reduce speckle originating from the intrinsic coherence of a supercontinuum laser used for reflectance spectroscopy. Rotating the samples and spectral averaging reveals the characteristic spectra of two minerals.
Measurement-feedback control of the chiral photon emission from an atom chain into a nanofiber

We theoretically investigate measurement-based feedback control of a laser-driven one-dimensional atomic chain interfaced with a nanofiber. The interfacing leads to all-to-all interactions among the atomic emitters and induces chirality (i.e., the directional emission of photons into a preferred guided mode of the nanofiber). In the setting we consider, the measurement of guided light—conducted either by photon counting or through homodyne detection of the photocurrent quadratures—is fed back into the system through modulation of the driving laser field. We investigate how this feedback scheme allows control of the statistics of the photon counting and the quadratures of the light, as well as the many-body state of the atom chain. In particular, we identify regimes where both the photon counting rate and its fluctuations are dramatically enhanced. Moreover, we find that the action of homodyne detection feedback allows the alteration of the stationary state of the chain from a pure, dimer state, to a fully mixed one. Our results provide insights on how to control and engineer dynamics in light–matter networks realizable with state-of-the-art experimental setups.
Optical single-shot spatial state tomography

We propose a device for measuring the spatial state of a beam, whereby all required measurements to construct the density matrix are performed single-shot using lossless components, rather than through sequential projections. © 2020 The Author(s)
Search Engine Similarity Analysis: A Combined Content and Rankings Approach

How different are search engines? The search engine wars are a favorite topic of on-line analysts, as two of the biggest companies in the world, Google and Microsoft, battle for prevalence of the web search space. Differences in search engine popularity can be explained by their effectiveness or other factors, such as familiarity with the most popular first engine, peer imitation, or force of habit. In this work we present a thorough analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo, which goes to great lengths to emphasize its privacy-friendly credentials. To do so, we collected search results using a comprehensive set of 300 unique queries for two time periods in 2016 and 2019, and developed a new similarity metric that leverages both the content and the ranking of search responses. We evaluated the characteristics of the metric against other metrics and approaches that have been proposed in the literature, and used it to (1) investigate the similarities of search engine results, (2) the evolution of their affinity over time, (3) what aspects of the results influence similarity, and (4) how the metric differs over different kinds of search services. We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.
Machine Learning Approach to Chirp Rate Estimation of Linear Frequency Modulated Radars

The detection and parametric estimation of low-SNR radar signals, particularly linear frequency modulated (LFM) radar signals, is a problem of considerable interest. In prior work, this problem has been investigated using various signal processing techniques, such as maximum likelihood estimation, fractional Fourier transform and Wigner-Ville-based methods, to analyze the signal parameters of a complex linear frequency modulated signal. Other work has focused on applying deep learning to automatically recognize various radar waveform types and their features, such as linear frequency modulation (LFM), Barker code and rectangular waveforms. In this paper, we investigate this problem from a machine learning perspective for multiple LFM radar signals given a priori information. We explore the use of naive Bayes, support vector machine and neural network classifiers to identify the LFM chirp rate, out of a set of known chirp rates, from a specific radar emitter under varying SNR conditions. Simulation results demonstrate the viability of this technique to identify the radar LFM mode in very low signal-to-noise ratio conditions down to -20 dB where using existing approaches (e.g., Wigner-Ville) fail.
We are here and we are many: Using a telepresence robot for shared exploration and learning (and fun)

Abstract Mobile remote presence bots (MRP) have emerged as a potential way of addressing the “tyranny of distance” when having to attend meetings at far away locations. In this contribution we report on how we used an MRP to share with two cohorts of postgraduate students at a regional university the formal “conferencing” and the informal “mingling” that takes place at quality academic conferences and that many would consider essential for effective networking and knowledge sharing. Simultaneously, students were able to experience and explore what it meant to be “different” in a room full of people interacting in “regular” ways, observing the conference attendees reacting to the MRP aka “ipad on a stick” in ways from genuine interest to forced indifference.
A Three Stream Deep Network on Extracted Projected Planes for Human Action Recognition

Human actions are challenging to recognize as it varies its shape from different angle of perception. To tackle this challenge, a multi view camera set up can be arranged, however, it is not cost effective. To handle this issue, a multi stream deep learning network is proposed in this work which is trained on different 3D projected planes. The extracted projected planes which represents different angle of perception, are used as an alternative to multi view action recognition. The projected planes are such that they represents top, side and front view for the action videos. The projected planes are then fed to a three stream deep convolutional neural network. The network uses transfer learning technique to avoid training from scratch. Finally, the scores from three streams are fused to provide the final score to recognize the query video. To evaluate the proposed work, the challenging KTH dataset is used which is widely used and publicly available. The results show that the proposed work performs better compared to state-of-the-art techniques.
Giving semantics to program-counter labels via secure effects

Type systems designed for information-flow control commonly use a program-counter label to track the sensitivity of the context and rule out data leakage arising from effectful computation in a sensitive context. Currently, type-system designers reason about this label informally except in security proofs, where they use ad-hoc techniques. We develop a framework based on monadic semantics for effects to give semantics to program-counter labels. This framework leads to three results about program-counter labels. First, we develop a new proof technique for noninterference, the core security theorem for information-flow control in effectful languages. Second, we unify notions of security for different types of effects, including state, exceptions, and nontermination. Finally, we formalize the folklore that program-counter labels are a lower bound on effects. We show that, while not universally true, this folklore has a good semantic foundation.
Covid-19 telescreening in SUS users with risk conditions

This case report aims to describe the conception and preliminary data of the implementation of a telescreening and telemonitoring program of covid-19 for users of the Unified Health System with risk conditions. A system of telerscreening was implemented through which undergraduate students in the health area contact patients by telephone, according to periodicity and predefined criteria, to monitor the evolution of the condition. In eight weeks, 2,190 attempts at remote contact were made with individuals from five health units. The effective number of individuals monitored at the time this writing is 802.
Face Detection Method Based on Improved Convolutional Neural Network

Nowadays, the number of Internet image data is increasing rapidly, so it is urgent to understand image information by corresponding intelligent system. The purpose of face detection is to determine the location and size of the face in an image, which is one of the main research directions in the field of computer vision. Under certain constraints, face detection technology has achieved very high performance, but under the unconstrained conditions, many existing algorithms just change the network model structure, but can't meet the actual needs. In view of the above problems, this paper proposes a face detection method region based on multi-resolution full convolution neural network and convolution neural network. In this method, from the multi-scale point of view, a multi-resolution sliding window is used to generate a multi-level resolution human face heat map. According to the local hottest region on the heat map, the face candidate region is obtained. Finally, the face candidate region is sent to CNN classification network for classification, and the face position is obtained. The experimental results show that, the method proposed in this paper improves the detection rate of human face effectively and has better practicability.
StreetConqAR: Augmented Reality Anchoring in Pervasive Games

Pervasive games have increased in popularity with the rise of location-based games, but their content quality varies based on the area it is played in. We present a system of anchoring street signs into an approach based on augmented reality, allowing a game being played in all rural and urban areas without the need for custom content creation. By using publicly available map data, we allow for gameplay across city and country borders. Our approach identifies and augments street signs on a player’s camera feed. It then automatically creates an AR challenge, based on the classical game Mastermind, for the player to capture the virtual street. The virtual streets are integrated into one game world where players can conquer and take virtual ownership of whole blocks, districts, or cities. We achieve correct augmentation rates of 84.7% with initial delays of one second and continuous fluent augmentation.
MiTE: Program Penyunting Topologi Jaringan untuk Pembelajaran SDN

Software-defined networking (SDN) is a network programmability concept that separates the control plane from the data plane by proposing a centralized control plane called a controller. Thus, network administrators are able to program the entire networks and their components via the controller. However, learning SDN is challenging due to its complex network setup and the different types of SDN networks such as OpenFlow, and P4. To ease the learning curve, the use of network emulation and a graphical-based network editor is necessary. This paper discusses the implementation of such an application, called MiTE. It satisfies both requirements: a visual network editor enforced with a configuration generator for emulation purpose. We evaluate the program by implementing IP routing cases for both, OpenFlow-based and P4-based networks. The result shows that both cases can be created easily by using a mouse command. The program has more interactive user interface while the created topologies are more informative. Compared to similar applications, our proposed application has better support for a wider range of SDN networks (Openflow and P4), fine-grain network configuration, and a more informative user interface.&#x0D;
Improved Particle Swarm Optimization Based on Gradient Descent Method

Due to the lack of effective guidance on particle's speed and precocity in standard particle swarm optimization, a particle swarm optimization on the basis of gradient information and time-varying acceleration coefficient (TVAC), namely gradient descent particle swarm optimization (GDPSO), is proposed. By combining the direct method and the indirect method to solve the unconstrained optimization problem, the gradient information is used to modify the velocity term, guide the particle to conduct local efficient search, and improve the global explore ability of the algorithm through time-varying acceleration coefficient strategy. On the basis of simulation experiment and comparison with other algorithms, the proposed particle swarm optimization enjoys a fast convergence speed and is not easy to get trapped into local optimal, with excellent ability to solve complex multi-modal problems.
Extrapolating False Alarm Rates in Automatic Speaker Verification

Automatic speaker verification (ASV) vendors and corpus providers would both benefit from tools to reliably extrapolate performance metrics for large speaker populations without collecting new speakers. We address false alarm rate extrapolation under a worst-case model whereby an adversary identifies the closest impostor for a given target speaker from a large population. Our models are generative and allow sampling new speakers. The models are formulated in the ASV detection score space to facilitate analysis of arbitrary ASV systems.