Miracle-dz/UniFAR-FTUs huggingface dataset:
A String-Based Public Key Cryptosystem

Traditional methods in public key cryptography are based on number theory, and suffer from problems such as dealing with very large numbers, making key creation cumbersome. Here, we propose a new public key cryptosystem based on strings only, which avoids the difficulties of the traditional number theory approach. The security mechanism for public and secret keys generation is ensured by a recursive encoding mechanism embedded in a quasi-commutative-random function, resulted from the composition of a quasi-commutative function with a pseudo-random function. In this revised version of the paper we show that the eavesdropper's problem of the proposed cryptosystem has a solution, and we give the details of the solution.
A LS-SVM Based Nonlinearity Compensator for Thermocouple Vacuum Gauge

The input and output characteristic of thermocouple vacuum gauge exhibits high nonlinearity, which comes from the gauges' inherent physical peculiarities and causes big error in measurement results. There is a need to make compensation of the nonlinearity for accurate measurement. In this article, a least squares support vector machine (LS-SVM) model is developed to function as a compensator with which to accomplish this task. Particle swarm optimization (PSO) algorithm is introduced to search optimal parameters combination of the LS-SVM model. Based on the real sensor measurements obtained from a solar water heater plant, experiments were performed to investigate the compensation accuracy with developed LS-SVM compensator model and higher compensation accuracy results were obtained.
Incorporating multi-task learning in conditional random fields for chunking in semantic role labeling

This paper presents a novel application of incorporating Alternating Structure Optimization (ASO) to conduct the task of text chunking of Semantic Role Labeling (SRL) in Chinese texts. ASO is a competent linear algorithm based on the theory of Multi-task Learning. In this paper, by constructing several SRL tasks to constitute a multi-task, we are able to encode the inference obtained by ASO algorithm as additional feature to further boost the performance of the target task employing Conditional Random Fields (CRFs). To our knowledge, our method is the first that incorporates multi-task learning into a statistical model in SRL for Chinese texts. We evaluate our approach on Penn Treebank data sets and obtain encouraging result.
Recognition of Handwritten Odia Numerals Using Artificial Intelligence Techniques

Creation of an accurate and efficient recognition system for unconstrained handwritten characters has been a subject of intensive research. Till date, no technique is available that can recognize with 100% accuracy for all type of data sets that may be observed in real-life. That is why research is still on for finding a robust technique capable of dealing with natural variations in handwritten characters. In this paper, the suitability of Artificial Neural Network (ANN) model with back-propagation learning has been studied in recognizing the handwritten numerals of Odia language (an Indian regional language). LU decomposition of matrix has been used to extract the feature vectors from the character image.
Size calculation methods for remote obstacles based on line structured light sensor

UGVs are required to have the functions of environmental awareness, recognition and self-avoidance obstacles. Recognition is one important part. In this paper, size calculation methods for obstacles based on a new line structured light sensor are proposed. The detecting distance of this sensor for concave and convex obstacles and water hazards is about 100 meters. Recognition principles of obstacles are analyzed, and calculation equations for different types of obstacles are proposed. Finally, the feasibility of obtaining the obstacle feature information is verified by experiments. And the calculation methods have the advantages of strong recognition, more targeted and fast.
Summarizing Social Image Search Results using Human Affects

In this paper, we propose the selection of representative images based on human affects. For this, the images are first transformed into the affective space using convolutional neural network (CNN). Thereafter, images are clustered on affective space and then the resulting clusters are ranked based on the proposed three properties ? coverage, affective coherence and distinctiveness. Finally, some representative images are selected from top-ranked clusters. The experiments conducted on Flickr images showed the effectiveness of the proposed method.
Empetrum nigrum var. japonicum Extract Suppresses Ultraviolet B-Induced Cell Damage via Absorption of Radiation and Inhibition of Oxidative Stress

This study focused on the protective actions of Empetrum nigrum against ultraviolet B (UVB) radiation in human HaCaT keratinocytes. An ethyl acetate extract of E. nigrum (ENE) increased cell viability decreased by exposure to UVB rays. ENE also absorbed UVB radiation and scavenged UVB-induced intracellular reactive oxygen species (ROS) in HaCaT keratinocytes. In addition, ENE shielded HaCaT keratinocytes from damage to cellular components (e.g., peroxidation of lipids, modification of proteins, and breakage of DNA strands) following UVB irradiation. Furthermore, ENE protected against UVB-induced apoptotic cell death, as determined by a reduction in the numbers of apoptotic bodies and sub-G1 hypodiploid cells, as well as by the recovery of mitochondrial membrane potential. The results of the current study therefore suggest that ENE safeguards human keratinocytes against UVB-induced cellular damage via the absorption of UVB ray and scavenging of UVB-generated ROS.
Ethnic and Gender Influences on Adaptive Behaviour of some Nigerian Children

In this manuscript we summarize the results of a pilot study carried out to field test a newly developed adaptive behaviour scale for children that is culturally sensitive and expected to be culturally appropriate for Nigerian children. In addition we attempted to determine whether adaptive behaviour skill competence increased with age and the roles played by ethnicity and gender in the acquisition and demonstration of adaptive behaviour skills. The children were sampled from the three Nigerian major ethnic groups namely Yoruba, Igbo, and Hausa who are resident in Lagos, Nigeria. 282 children aged 2-9 years were rated by their class teachers using the newly developed adaptive behaviour scale for children [3]. The results obtained from this study indicated that: (i) the development and demonstration of adaptive behaviour skill competence increased with age; (ii) at particular age group levels, ethnicity significantly and differentially increased adaptive behaviour skill competence in all the skill domains; (iii) in the communication domain and also at particular age group levels gender significantly and differentially increased adaptive behaviour skill competence of the children. There was no significant main effect for ethnicity and gender. The scale items were found to be culturally appropriate for Nigerian children. It was therefore concluded that further research is needed to establish an overall influence of ethnicity and gender on the acquisition and demonstration of adaptive behaviour skill competence in Nigerian children.
RECENT DEVELOPMENTS IN MATCHING THEORY AND ITS PRACTICAL APPLICATIONS

In recent years, many developments have been made in matching theory and its applications to market design. This paper surveys them and suggests possible research directions. The main focus is on the advances in matching theory that try to solve market design problems in practical markets where the classic theory is inapplicable. Specifically, we discuss the recent theory of matching in large markets and “approximate market design,” and a new theory of “matching with constraints,” as well as their applications. JEL Classification Numbers: C78, D47, D71.
Edge Proximity Conditions for Extendability in Planar Triangulations

A graph G with at least 2m+2 vertices is said to be distance d m‐extendable if for any matching M of G with m edges in which the edges lie at distance at least d pairwise, there exists a perfect matching of G containing M. In this article we prove that every 5‐connected triangulation on the plane of even order is distance 3 9‐extendable and distance 4 m‐extendable for any m.
Neuronvisio: A Graphical User Interface with 3D Capabilities for NEURON

The NEURON simulation environment is a commonly used tool to perform electrical simulation of neurons and neuronal networks. The NEURON User Interface, based on the now discontinued InterViews library, provides some limited facilities to explore models and to plot their simulation results. Other limitations include the inability to generate a three-dimensional visualization, no standard mean to save the results of simulations, or to store the model geometry within the results. Neuronvisio (http://neuronvisio.org) aims to address these deficiencies through a set of well designed python APIs and provides an improved UI, allowing users to explore and interact with the model. Neuronvisio also facilitates access to previously published models, allowing users to browse, download, and locally run NEURON models stored in ModelDB. Neuronvisio uses the matplotlib library to plot simulation results and uses the HDF standard format to store simulation results. Neuronvisio can be viewed as an extension of NEURON, facilitating typical user workflows such as model browsing, selection, download, compilation, and simulation. The 3D viewer simplifies the exploration of complex model structure, while matplotlib permits the plotting of high-quality graphs. The newly introduced ability of saving numerical results allows users to perform additional analysis on their previous simulations.
Complementary Circuits for On-Line Detection for 1-out-of-3 Codes

In this paper a new method for concurrent checking of an arbitrarily given combinational multi-output circuit f by use of an 1-out-of-3 code is presented. The circuit f is completed by an additional multi-output circuit g in such a way that the XOR-sums of the corresponding outputs of f or of the inverted outputs of f and g are elements of a 1-out-of-3 code. For efficiently checking the 1-out-of-3 encoded outputs also a new self-checking checker based on a three-rail checker cell is proposed.
Model transformation and scheduling analysis of an AUTOSAR system

AUTOSAR standard provides a common framework for software development in the automotive domain. It enables to manage the growing of the automotive architecture complexity by facilitating the integration and reuse of software components. However, additional work is needed to enable scheduling analysis and to handle with more timing properties in the system. In this paper, we propose an approach to enable a model transformation of the AUTOSAR timing model to a classical scheduling one. This allows to apply directly fundamentals scheduling theories for timing analysis. Then, we apply our approach through a steering-by-wire case study. Finally, we analyze the results given by the holistic algorithm and those given by a compositional one.
On the Integrity of Deep Learning Oracles

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.
Musical Sound Quality Impairments in Cochlear Implant (CI) Users as a Function of Limited High-Frequency Perception

The purpose of this study was to (a) apply the musical sound quality assessment method, Cochlear Implant-MUltiple Stimulus with Hidden Reference and Anchor (CI-MUSHRA), to quantify musical sound quality deficits in CI (cochlear implant) users with respect to high-frequency loss, and (b) assess possible correlations between CI-MUSHRA performance and self-reported musical sound quality, as assessed by more traditional rating scales. Five versions of real-world musical stimuli were created: 8-,4-, and 2-kHz low-pass-filtered (LPF) versions with increasing high-frequency removal, a composite stimulus containing a 1-kHz LPF-filtered version and white noise (“anchor”), and an unaltered version (“hidden reference”). Using the CI-MUSHRA methodology, these versions were simultaneously presented to participants in addition to a labeled reference. Participants listened to all versions and provided ratings based on a 100-point scale that reflected perceived sound quality difference among the versions. A total of 25 musical stimuli were tested. As comparison measures, participants completed four Visual Analogue Scales (VAS) to assess musical sound quality. Overall, compared to normal hearing (NH) listeners, CI users demonstrated an impaired ability to discriminate between unaltered and altered musical stimuli with variable amounts of high-frequency information removed. Performance using CI-MUSHRA to evaluate this parameter did not correlate to measurements of musical sound quality, as assessed by VAS. This study identified high-frequency loss as one acoustic parameter contributing to overall CI-mediated musical sound quality limitations. CI-MUSHRA provided a quantitative assessment of musical sound quality. This method offers the potential to quantify CI impairments of many different acoustic parameters related to musical sound quality in the future.
A relaxed-criteria bargaining protocol for grid resource management

Since participants in a computational grid may be independent bodies, some mechanisms are necessary for resolving their differences. The contribution of this work is designing a bargaining protocol for agents representing grid participants in negotiating for computational resources. The protocol in this work discerns from existing protocols because agents in this work are programmed to follow a set of fuzzy rules for relaxing bargaining terms with the hope of increasing their chance of successfully acquiring resources before their deadlines. This relaxed-criteria bargaining protocol and an agent-based testbed for simulating grid resource allocation were implemented in Java 1.5. A series of simulations was carried out using the testbed, and favorable (preliminary) empirical results show that following the relaxed-criteria bargaining protocol, agents achieved higher success rates in negotiating for resources
Tile pattern KL-divergence for analysing and evolving game levels

This paper provides a detailed investigation of using the Kullback-Leibler (KL) Divergence as a way to compare and analyse game-levels, and hence to use the measure as the objective function of an evolutionary algorithm to evolve new levels. We describe the benefits of its asymmetry for level analysis and demonstrate how (not surprisingly) the quality of the results depends on the features used. Here we use tile-patterns of various sizes as features. When using the measure for evolution-based level generation, we demonstrate that the choice of variation operator is critical in order to provide an efficient search process, and introduce a novel convolutional mutation operator to facilitate this. We compare the results with alternative generators, including evolving in the latent space of generative adversarial networks, and Wave Function Collapse. The results clearly show the proposed method to provide competitive performance, providing reasonable quality results with very fast training and reasonably fast generation.
Towards high repetition rate operational quantum relay at telecom wavelength

Quantum entanglement, besides being a theoretical ground in quantum physics, is now considered as a key resource for the implementations of many complex quantum information science (QIS) protocols. One of the most investigated QIS protocols for long-distance quantum communication, involving entanglement as a genuine resource, is the entanglement swapping in quantum relay (QR) configuration. Alas, the quality of a QR is heavily affected by synchronization issues between remote entangled photon pair sources. In this paper, we present an all-optical, high repetition rate synchronization technique in picosecond regime, and demonstrate the realization of ultra-fast photon pair sources based on high-performance telecom components and nonlinear optics.
DeepOPF: Deep Neural Network for DC Optimal Power Flow

We develop DeepOPF as a Deep Neural Network (DNN) based approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions. We construct and train a DNN model to learn such mapping, then we apply it to obtain optimized operating decisions upon arbitrary load inputs. We adopt uniform sampling to address the over-fitting problem common in generic DNN approaches. We leverage on a useful structure in DC-OPF to significantly reduce the mapping dimension, subsequently cutting down the size of our DNN model and the amount of training data/time needed. We also design a post-processing procedure to ensure the feasibility of the obtained solution. Simulation results of IEEE test cases show that DeepOPF always generates feasible solutions with negligible optimality loss, while speeding up the computing time by two orders of magnitude as compared to conventional approaches implemented in a state-of-the-art solver.
The N 400 Is Not a Semantic Anomaly Response : More Evidence from Adjective-noun Combination

Previous work has shown that the N400 ERP component is elicited by all words, whether presented in isolation or in structured contexts, and that its amplitude is modulated by semantic association and contextual predictability. Although these data suggest that the N400 reflects core semantic computations involved in processing any meaningful stimulus, the broader literature continues to maintain the alternative idea that the N400 response primarily indexes the recognition of semantic incongruity, in part because many designs have confounded predictability with congruity. In the current study, we evaluate the hypothesis that the N400 response indexes semantic anomaly with an adjective-noun paradigm that allows us to precisely control predictability through corpus counts. In three experiments, we find small and unreliable N400 effects of semantic congruity (yellow bag vs. innocent bag), and yet we find massive and reliable N400 effects of predictability (runny nose vs. dainty nose) under the same conditions. While these data alone cannot determine the functional interpretation of the N400 effect, they provide very clear evidence against the common characterization that ‘semantic anomaly’ is one of its primary determinants.
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces

Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a framework for solving complex domains that require combining high-level actions with flexible control. The recent P-DQN algorithm extends deep Q-networks to learn over such action spaces. However, it treats all action-parameters as a single joint input to the Q-network, invalidating its theoretical foundations. We analyse the issues with this approach and propose a novel method, multi-pass deep Q-networks, or MP-DQN, to address them. We empirically demonstrate that MP-DQN significantly outperforms P-DQN and other previous algorithms in terms of data efficiency and converged policy performance on the Platform, Robot Soccer Goal, and Half Field Offense domains.
Unified Generator-Classifier for Efficient Zero-Shot Learning

Generative models have achieved state-of-the-art performance for the zero-shot learning problem, but they require re-training the classifier every time a new object category is encountered. The traditional semantic embedding approaches, though very elegant, usually do not perform at par with their generative counterparts. In this work, we propose an unified framework termed GenClass, which integrates the generator with the classifier for efficient zero-shot learning, thus combining the representative power of the generative approaches and the elegance of the embedding approaches. End-to-end training of the unified framework not only eliminates the requirement of additional classifier for new object categories as in the generative approaches, but also facilitates the generation of more discriminative and useful features. Extensive evaluation on three standard zero-shot object classification datasets, namely AWA, CUB and SUN shows the effectiveness of the proposed approach. The approach without any modification, also gives state-of-the-art performance for zero-shot action classification, thus showing its generalizability to other domains.
Parallel Estimation of Translation for Video Stabilization

Translation motion estimation is the major aspect of video motion compensation, especially in two-dimensional turntable. This paper presents a robust parallel estimation algorithm of translation in video stabilization system. The equivalent model of translation that derived is accorded with small rotation angle comparatively. A parallel algorithm based on grey mapping is used in local motion estimation to shorten the estimation time and improve the computation efficiency. After local motion vectors are calculated, a fast filter can detect the spurious vectors avoiding possible jerks. The suggested translation estimation algorithm has been implemented on NVIDIA GTX 260, the experimental results show that , the average time of the estimation and compensation finished in 16 ms/frame, and stabilize 1k×1k pixels sequence gray video up to 60 frames/s.