Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Motion Planning for an EOG-centered Robot Assisted Navigation in a Virtual Environment

This paper presents a new collaborative approach for robot motion planning of an assistive robotic platform that takes into account the intentions of the user provided through Electrooculographic (EOG) signals, as well as obstacles surrounding the robotic platform. In order to increase human confidence in the operation of robotic platforms with some degree of navigational autonomy, the intent of the user must be included in the decision process. In our system, the humanrobot interface works through ocular movements (saccades and blinks), which are acquired as EOG signals and classified using a Convolutional Neural Network. In our proposed approach, a model-free Reinforcement Learning (RL) layer is used to provide commands to a virtual robotic platform. The RL layer is constantly being updated with the inputs from the user's intent, environment perception and previous machine-based decisions. In order to prevent collisions, machine-based perception using the proposed RL motion planning approach will assist the user by selecting suitable actions while learning from prior driving behaviors. The approach was validated by a set of tests that consisted of driving a robotic platform in an in-house 3D virtual model of our Research Center (ISR-UC). The experimental results show a better performance of the proposed approach with RL when compared to the version without the RL-based motion planning component. Results show that the approach is a promising step in the concept put forward for collaborative Human-Robotic Interaction (HRI), and opens a path for future research.
A Wheelchair Simulator Using Limited-Motion Patterns and Vection-Inducing Movies

Virtual reality (VR)-based wheelchair simulators have been used for rehabilitation and training, since a VR-based approach is more feasible than constructing physical courses for wheelchairs. However, existing VR-based wheelchair simulators have difficulty providing both visual and motion feedback at low cost. To address this issue, we propose a VR-based wheelchair simulator using a combination of motions attainable by an electric-powered wheelchair and vection-inducing movies displayed on a head-mounted display. This approach enables the user to have a richer simulation experience, because the scenes of the movie change as if the wheelchair performs motions that are not actually performable. Also, this system can be implemented only consumer products so as to keep cost low. We developed a proof of concept and conducted precise evaluation tasks, confirming that our approach can provide a richer experience for barrier simulations.
To Err...

As noted science fiction author Robert Heinlein once said: "Man is not a rational animal, he is a rationalizing animal." [3] As recent work in behavioral economics has made clear, our irrationality is not random; we are irrational in predictable ways [4]. Despite this, many "human-centered" systems do not leverage this predictable irrationality in a way that is potentially productive for users. Hypertext research has its roots in the field of human computer interaction. Some of the most influential early writers in hypertext (e.g., Bush [1] and Engelbart [2]) described hypertext systems as ones that would extend and augment human capabilities. This foundational perspective on hypertext is still well-evidenced in some of the most recent research. Bush focused largely on extending human memory; Engelbart on augmenting human intellect. For these two pioneers, as well as a host of other hypertext researchers, the key aspect of hyper-text that allows it to augment humans so well is its rich under-standing of structure and the computations in and around it. It is practically axiomatic within the field that this rich structure is somehow closer to "how humans think," though, as with most axioms, exactly why or how this is so is never fully and satisfactorily explained. Instead, the hypertext field relies on argument from example: non-linear traversals of information spaces (as opposed to an artificial linearity imposed by technologies such as writing) or emergent spatial organization of information (as opposed to prematurely formal organizations such as outlines). These examples (and others) work from generally "correct" forms of human thinking. Hypertext researchers, for example, do not claim that non-linear traversals of information spaces augment human intellect because some of the traversed links are faulty. On the contrary, linear representations are in some sense "defective" because they cannot represent the inherently non-linear thinking engaged in by humans, this "defectiveness" apparently attributable to a failure to capture useful associations that are (or could usefully be) present in the mind of the traverser. However, if fidelity of representation is the basis of the belief that hypertext can augment human thought, should we not also build into our understanding of structure the predictable biases we know are present? In this talk, we will explore the idea of accounting for the types of predictable irrationality discussed by behavioral economists in the understanding of hypertext structure. How can we model our biases? Does this bring us closer to accurate representations of human thought? Can we, in turn, find new applications for hypertext based not only on non-linear, emergent and rich structures, but ones that are flawed in a predictable way?
A compressed dynamic self-index for highly repetitive text collections

We present a novel compressed dynamic self-index for highly repetitive text collections. ESP-index is a static self-index of this type and has a large disadvantage of slow pattern search for short patterns. We obtain faster pattern search by leveraging the idea behind a truncated suffix tree (TST) to develop the first compressed dynamic self-index, called TST-index, that supports not only fast pattern search but also dynamic update operations for highly repetitive texts. Experiments with a benchmark dataset show that the pattern search performance of the TST-index is significantly better than that of ESP-index for short patterns.
Local Feature Descriptor Learning with a Dual Hard Sampling Strategy

Local feature descriptor learning based on Convolutional Neural Network (CNN) has demonstrated its capability to generate descriptors with high quality. While extensive studies focused on mining hard non-matching examples to improve descriptor learning performance, a random sampling strategy is adopted for matching examples. In this paper, a dual hard sampling strategy based on the triplet loss function is proposed to generate the hard matching and non-matching examples for training. To start with, a pair of matching examples with the maximum distance for each class are selected as the positive pair. For each positive pair, their closest non-matching example is then sampled from the generated positive pairs with other classes as the corresponding negative. Based on the above dual hard sampling strategy, a novel triplet loss function is presented for optimization. With the benefits of the proposed sampling strategy and the novel triplet loss function, our method achieves better performance compared to state-of-the-art on the reference benchmark for local feature matching.
Verification of Applying Curiosity-Driven to Fighting Game AI

In this paper, we apply a curiosity-driven intrinsic reward to reinforcement learning (RL) in a fighting game and verify its effectiveness. An actor-critic model is used for RL. Our experimental results show that the proposed AI has a better learning ability than an AI using a standard actor-critic model.
Hybrid Structure Based PSO for ESN Optimization

Recently, the echo state networks (ESNs) have been widely studied. In an ESN, the input weights and internal weights of reservoir are fixed after initialization, only the output weight matrix needs to be optimized. To calculate the output weights of ESN, the particle swarm optimization algorithm (PSO) with hybrid topology is proposed. The structure of the proposed PSO is mixed with regular network with strong exploration ability and scale-free network with good exploration ability. Simulation results show that the proposed ESN has good prediction performance than the traditional ESN.
Semantic Content Analysis: A New Methodology for the RELATUS Natural Language Environment

This chapter introduces semantic content analysis, a methodology whose vehicle is automatic recognition and classification of instances in the knowledge representations of texts, or text models. Semantic content analysis differs from traditional content analysis because it operates on referentially integrated text models. Referential integration means that references to the same object or relation, which may appear in different sentences of a text, are resolved and represented as the same semantic node. Semantic perception is the process of mapping from a syntactic representation into a semantic representation. In RELATUS the construction of semantic representations from canonical grammatical relations and the original lexical items is informed by a theory of lexical-interpretive semantics. The RELATUS system reaches the level of eidetic representation and even somewhat beyond. RELATUS gains broad coverage and domain-independence from a bottom-up strategy that combines a general syntactic analysis with a constraint-posting reference system to create large, referentially integrated semantic representations.
MCDM method for Financial Fraud Detection

Financial fraud has a big impact on the financial sector, governments, companies and ordinary consumers. With reliance on new technologies such as cloud and mobile computing, the impact of this problem has become very dangerous. The overall losses caused by financial fraud are uncountable, which makes its detection indispensable to prevent the devastating consequences of fraud. MCDM methods are proposed as a solution of financial fraud detection (FFD) problems. The main objective of this paper is to introduce the types of financial fraud, to discover what challenges need to be solved to reduce losses and to present a state-of-the-art of the MCDM approaches dedicated to FFD that allow evaluation and selection of FFD models.
SAGA: Secure Auto-Configurable Gateway Architecture for Smart Home

Advances in the Internet of Things (IoT) have changed Smart Home technology from a local control system to one where many devices with high computational ability are network-enabled. These devices exhibit known and unknown attack surfaces by attackers either compromising the Smart Home itself or exploiting these devices to launch DDoS attacks. However, average home users often lack the technical skills to properly protect their networks and devices from those attacks. And manufacturers tend to design their systems for ease of use rather than ensuring security. Even for skilled home users, secure manual configuration and connection are time-consuming and error-prone. Therefore, it is important to develop a technique which is both simple to manage and secure against cyber-attacks. We present a secure auto-configurable gateway architecture (SAGA) that allows IoT devices to be automatically discovered, registered and configured when they connect to a Smart Home network. SAGA can (1) automatically register IoT devices securely, (2) reject malicious attempts by rogue devices to connect to the network, (3) identify attempts to capture devices by a rogue gateway and (4) provide services such as firewalls, address translation, device software updates, and host home control applications. Security analysis for the proposed architecture and details was performed. A prototype for SAGA was implemented and performance was evaluated in terms of execution/processing time.
Binary Sequence Generated by Alternative Trace Map Function and Its Properties

In this paper, we propose a new pseudo random binary sequence generated from a primitive polynomial over an extension field. The sequence has maximum period and high linear complexity suitable for security applications. While generation, a new mapping function is proposed which is faster than conventional trace map and translates extension field elements to prime field elements. The output of mapping function is a p-ary sequence with elements 0~ (p-1) that is balanced in pattern distribution and has ideal autocorrelation. Then, a binarization method is utilized to derive binary sequence from p-ary sequence. Period, autocorrelation, linear complexity and computation time of the proposed sequence are studied and experimental results are presented. Finally, the efficiency of the proposal is verified by comparing with existing sequences in terms of period, linear complexity and generation time for sequences of different length.
iTrustSO

Despite the apparent benefits of modern social coding paradigm such as Stack Overflow, its potential security risks have been largely overlooked (e.g., insecure codes could be easily embedded and distributed). To address this imminent issue, in this paper, we bring a significant insight to leverage both social coding properties and code content for automatic detection of insecure code snippets in Stack Overflow. To determine if the given code snippets are insecure, we not only analyze the code content, but also utilize various kinds of relations among users, badges, questions, answers and code snippets in Stack Overflow. To model the rich semantic relationships, we first introduce a structured heterogeneous information network (HIN) for representation and then use meta-path based approach to incorporate higher-level semantics to build up relatedness over code snippets. Later, we propose a novel hierarchical attention-based sequence learning model named CodeHin2Vec to seamlessly integrate node (i.e., code snippet) content with HIN-based relations for representation learning. After that, a classifier is built for insecure code snippet detection. Integrating our proposed method, an intelligent system named iTrustSO is accordingly developed to address the code security issues in modern software coding platforms. Comprehensive experiments on the data collections from Stack Overflow are conducted to validate the effectiveness of our developed system iTrustSO by comparisons with alternative methods.
Efficient convolutional neural network for apparent age prediction

This paper proposes an efficient convolutional neural network architecture for apparent age estimation from single face image capable of top performance without the use of a pretraining stage. The architecture consists of 9 convolution layers and 2 max pool layers and requires only 79k parameters. We also improve the results by applying a weighted class distribution which assures that overwhelmingly represented classes do not skew the prediction results. These are compared to other results in literature for both methods that use pretraining and those that do not. The proposed method achieves estimation errors comparable to the human reference and to existing methods while being characterized by efficiency at both training and testing, it employs orders of magnitude fewer parameters and much less training time than other state-of-the-art methods.
Performance Comparison of Filter-Based Approaches for Display of High Dynamic Range Hyperspectral Images

This paper presents performance comparison of nonlinear edge-preserving filters namely bilateral filter and weighted least square filter on improved visualization for display of high dynamic range hyperspectral image on low dynamic range media. This filter is used to decompose image into components of base and detail. The contrast of base image is reduced and details are preserved in detail component. Image construction is made by combining these two components. Performance analysis revealed that the bilateral filter-based approach performs better than the conventional average method and the weighted least square filter in terms of normalized average gradient and root mean square contrast.
Generating Homeric Poetry with Deep Neural Networks

We investigate the generation of metrically accurate Homeric poetry. We assess the quality of the generated lines of poetry using quantitative metrical analysis and expert evaluation. This evaluation reveals that our model captures complex poetic meter implicitly, but underperforms in terms of semantics and context matching. Our research highlights the importance of expert evaluation and shows that future research should focus on poetic semantics.
Introduce GIoU into RFB net to optimize object detection bounding box

RFB algorithm is a very advanced algorithm in the direction of Object Detection based on Deep Learning, RFB Net proposed RFB module to simulate Receptive Fields (RFs) in human visual systems and gain higher accuracy. RFB, however, also have other Object Detection algorithm of faults, that is the detection of choosing predicted bounding box is a box with the highest scores, rather than the most close to the truth boundary box, and the positioning accuracy of bounding box is a core problem, therefore, we put forward a kind of method is replaced IoU(Intersection over Union) with GIoU as predicting boundary box plus or minus a sample selection criteria which can improve the prediction location of bounding box, GIoU (Generalized Intersection over Union) is an improvement over IoU, based on the function of IoU, GIoU considers the offset angle, the distance of truth boundary box and predict boundary box to some extent. We try to replace IoU with GIoU as the discriminant criterion. In the original paper of GIoU algorithm, it is applied to two-stage algorithm, in one-stage algorithm, it only validation in YOLO, while this paper is applied to one-stage algorithm of RFB. Extensive experiments on recognition benchmarks like Pascal VOC2007, Pascal VOC2012, we showed the advantages of using GIoU instead of IoU.
Design of University Evaluation System based on Big Data

Abstract In the practice of early university evaluation, due to the backwardness of information technology and evaluation methods, the following problems were brought about: the social needs were neglected, the evaluation subject was single, the evaluation method was simple, and the evaluation data source was single. In the paper a big data model of university evaluation is presented, which studies the educational activities from 8 dimensions: core performance, innovation ability, teamwork, resources, audience willingness, parental evaluation, employer evaluation and social contribution. The model takes account of long-term, short-term factors, individual and overall factors, internal and external factors, educators and audience factors, etc., and carries out omnidirectional analysis. Every dimension is analyzed from 2 aspects: positive and negative. Then based on the model, an University Evaluation System based on Big Data (UESBD) was designed and implemented. Finally, the software was used to analyze the data of 20 colleges and universities from multiple dimensions to evaluate the educational activities of education.
A Label-Based Approach for Automatic Identifying Adversarial Examples with Image Transformation

Besides extraordinary results that deep neural networks are widely deployed in many fields such as computer vision, speech recognition, and natural language processing, in recent years deep neural networks have been recognized vulnerable to intentional modification of legitimate inputs called adversarial examples. These patterns are almost indistinguishable from AI models and human perception. Adversarial examples' concern is rising dramatically and is attracted by many research's apprehensiveness because of its tremendous impact. Unfortunately, until now there is none of the defenses has been shown to be very effective. In this paper, we introduce a new defense strategy against adversarial examples by using a label-based end-to-end system. Our proposed defense system can mostly distinguish adversarial samples and benign images without human intervention. We exploit the important role of spatial domain in adversarial attacks and proposing a state-of-the-art method for detecting adversarial examples based on our observation. We evaluate our system's performance on a variety of standard benchmark datasets including MNIST and ImageNet. Our proposed method reached out detection rates in a range from 94.6% to 99.2% in many settings.
Ambiguity of Response Particles to Negative Utterances in Korean and English

This study explores the ambiguity of Korean response particles (RP) ung 'yes' and ani 'no' responding to long form negation (LFN), in comparison to the ambiguity of RPs to the so-called "high negation" and "low negation" in English.The results show that i) the ambiguity of Korean LFN is due to two possible interpretations, i.e., pragmatic and literal, ii) Korean RPs are anaphors, supporting Krifka's (2013) propositional anaphoric approach to English RPs, but iii) they refer to only one antecedent from the preceding proposition, that is, for the outermost NegP only, for both SFN (short form negation) and LFN.In contrast, the ambiguity of English high negation is two-fold.One type of ambiguity is due to the two possible interpretations of high negation, pragmatic versus literal, just like Korean; the other type of ambiguity occurs within the literal meaning of high negation and is due to the availability of multiple antecedents, just like with English low negation.
Clustering on Multidimensional Poverty Data using PAM and K-prototypes Algorithm : Case Study: Jambi Province 2017

Poverty is still a serious concern of the Indonesian government. Through the Multidimensional Poverty terminology, experts try to understand poverty with a more comprehensive approach. Using data from the 2017 National Socio-Economic Survey (SUSENAS) and Alkire-Foster method, this study measures poverty in terms of the various deprivations experienced by residents in Jambi Province. The dimensions used in this study consist of 3 dimensions, namely: health, education, and living standard. This study investigates the use of PAM (partitioning around medoids) and k-prototype and compares their effectiveness in clustering mixed data types, using poverty data from published governmental data. This study also examines the scalability of the PAM and K-prototypes algorithm against the number of clusters for a given number of observations. The performance evaluation is carried out by comparing the value of the silhouette coefficient (SC) from each clustering method. In this study, clustering with K-prototypes is 59 % better than PAM in term of the SC value. The scalability test has shown that the K-prototypes algorithm is faster than the PAM algorithm. Considering the SC value, we can conclude that the cluster formed is reasonable. The one-way ANOVA and Kruskal-Wallis test result shows that 13 out of 17 variables used are a statistically significant difference between the formed clusters.
Adaptive Audio Watermarking Algorithm Based on Wavelet Transform

Abstract Aiming at the problem of small capacity and poor robustness of audio watermarking information, an adaptive digital audio blind watermarking algorithm based on wavelet transform is proposed. In this algorithm, each frame of the audio signal is converted into a discrete transform by using Haar wavelet, and the fine and approximate components are obtained, and the component masking threshold is calculated. Finally, the watermark embedding position is selected by the adaptive algorithm, quantized and modulated in the audio. The intermediate and low frequency coefficients of the signal. The simulation results show that the proposed algorithm has some resistance to common signal processing attacks and is robust to resampling and low-pass filtering attacks.