Reviewerly/exHarmony huggingface dataset:
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay

We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning. To this end, we propose S-TRIGGER, a general method for Continual State Representation Learning applicable to Variational Auto-Encoders and its many variants. The method is based on Generative Replay, i.e. the use of generated samples to maintain past knowledge. It comes along with a statistically sound method for environment change detection, which self-triggers the Generative Replay. Our experiments on VAEs show that S-TRIGGER learns state representations that allows fast and high-performing Reinforcement Learning, while avoiding catastrophic forgetting. The resulting system is capable of autonomously learning new information without using past data and with a bounded system size. Code for our experiments is attached in Appendix.
An Efficient MRI Brain Tumor Segmentation by the Fusion of Active Contour Model and Self-Organizing-Map

Accurate detection of tumors in brain MR images is very important for the medical image analysis and interpretation. Tumors which are detected and treated in the early stage gives better long-term survival than those detected lately. This paper proposes a combined method of Self-Organizing –Map (SOM) and Active Contour Model (ACM) for the effective segmentation of the brain tumor from MR images. ACMs are energy-based image segmentation methods and they treat the segmentation as an optimization problem. The optimization function is formulated in terms of appropriate parameters and is designed such that the minimum value of its correspondence to a contour which is a near approximation of the real object boundary. The traditional ACMs depend on pixel intensity as well as very susceptible to parameter tuning and it turns out to be a challenge for these ACMs to deal the image objects of distinct intensities. Conversely, Neural Networks (NNs) are very effective in dealing inhomogeneities but usually results in noise due to the misclassification of pixels. Additionally, NNs deal the segmentation problems without objective function. Hence we proposed a framework for the brain tumor segmentation which integrates SOM with ACM and is termed as SOMACM. This works by exactly integrating the global information derived from the weights or prototypes of the trained SOM neurons to aid choosing whether to shrink or enlarge the present contour during the optimization process and is performed in an iterative way. The proposed method can deal with the images of complex intensity distributions, even in the presence of noise. Exploratory outcomes demonstrate the high accuracy in the segmentation results of SOMACM on different tumor images, compared to the ACM as well as the general SOM segmentation methods. Furthermore, the proposed framework is not highly sensitive to parameter tuning.
Reforming the Approach to Racial and Religious Hate Speech Under Article 10 of the European Convention on Human Rights

At present the European Court of Human Rights employs a two-track approach to racial hate speech and religiously offensive speech, respectively. Further, the jurisprudence under Article 10 of the European Convention on Human Rights currently privileges journalistic or scholarly, over creative or artistic, forms of religious criticism. However, in this article it is argued that the ‘gratuitously offensive’ test for religiously offensive speech requires reform, while a consistent approach to racial hate speech cases is equally necessary. By building on Waldron’s account of the harm in hate speech, a single Article 10 test for both racial and religious hate speech is proposed. This new test focuses on harm in the sense of seriously undermining the target’s assurance to a status of equal worth in the community. It abandons the unhelpful race/religion dichotomy in the Article 10 jurisprudence, and is more responsive to the political reality of tense public discourse surrounding issues of race and religion in Europe in the wake of the Syrian refugee crisis and Brexit.
Dual Attention Networks for Visual Reference Resolution in Visual Dialog

Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information. A problem called visual reference resolution involves these challenges, requiring the agent to resolve ambiguous references in a given question and find the references in a given image. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution. DAN consists of two kinds of attention networks, REFER and FIND. Specifically, REFER module learns latent relationships between a given question and a dialog history by employing a self-attention mechanism. FIND module takes image features and reference-aware representations (i.e., the output of REFER module) as input, and performs visual grounding via bottom-up attention mechanism. We qualitatively and quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing that DAN outperforms the previous state-of-the-art model by a significant margin.
Leakage-Resilient Identity-Based Encryption in Bounded Retrieval Model with Nearly Optimal Leakage-Ratio

We propose new constructions of leakage-resilient public-key encryption (PKE) and identity-based encryption (IBE) schemes in the bounded retrieval model (BRM). In the BRM, adversaries are allowed to obtain at most $$\ell $$ -bit leakage from a secret key and we can increase $$\ell $$ only by increasing the size of secret keys without losing efficiency in any other performance measure. We call $$\ell /|\mathsf {sk}|$$ leakage-ratio where $$|\mathsf {sk}|$$ denotes a bit-length of a secret key. Several PKE/IBE schemes in the BRM are known. However, none of these constructions achieve a constant leakage-ratio under a standard assumption in the standard model. Our PKE/IBE schemes are the first schemes in the BRM that achieve leakage-ratio $$1-\epsilon $$ for any constant $$\epsilon >0$$ under standard assumptions in the standard model. As previous works, we use identity-based hash proof systems (IB-HPS) to construct IBE schemes in the BRM. It is known that a parameter for IB-HPS called the universality-ratio is translated into the leakage-ratio of the resulting IBE scheme in the BRM. We construct an IB-HPS with universality-ratio $$1-\epsilon $$ for any constant $$\epsilon >0$$ based on any inner-product predicate encryption (IPE) scheme with compact secret keys. Such IPE schemes exist under the d-linear, subgroup decision, learning with errors, or computational bilinear Diffie-Hellman assumptions. As a result, we obtain IBE schemes in the BRM with leakage-ratio $$1-\epsilon $$ under any of these assumptions. Our PKE schemes are immediately obtained from our IBE schemes.
Multitaper MFCC and normalized multitaper phase-based features for speaker verification

One trend of current research activity in speaker verification systems focuses on features that capture complementary information to the Mel-frequency cepstral coefficients (MFCCs). In conventional speaker verification methods, MFCCs are usually computed from a single-tapered DFT spectrum. These methods use the power of the Fourier transform of the time-domain speech frames and ignore the phase component due to its large amount of uncertainty. The multitaper phase information has been also ignored when the multitaper MFCC method applies. We propose a phase information extraction method that normalizes the change variation in the multitaper phase. The goal of this work is to incorporate phase information using low-variance multitaper spectrum estimation method instead of conventional single-taper window and normalizes the change variation in multitaper phase according to the frame position of the input speech to reduce the uncertainty of multitaper phase information in both the state-of-the-art Gaussian mixture model-universal background model (GMM-UBM) baseline and the i-vector speaker verification system. The experiments are conducted on the TIMIT database for both the clean and noisy conditions (SNR = 10 dB). The multitaper inverted-MFCC is also added to the overall system to produce diversity with respect to the fused subsystem. The results show relative improvement of 34% and 25% in terms of equal error rate (EER) over the MFCC baseline in clean and noisy conditions for GMM-UBM system, respectively. The relative improvement in terms of EER with i-vector back-end are about 36% and 16% in clean and noisy conditions as well.
Joint optimization of tasks placement and routing to minimize Coflow Completion Time

Coflow is defined as the parrellel flows between two successive computation stages of data-parallel jobs. Reducing Coflow Completion Time (CCT) is important to improve the performance of data-parallel applications in geo-distributed datacenter networks. CCT is influenced by two factors: tasks placement and coflow routing. Previous works that optimize only one factor are insufficient in reducing CCT. In this paper, we explore the joint optimization of tasks placement and coflow routing to reduce CCT. We formulate the joint optimization of a single coflow as a Mixed Integer Non-Linear Programming problem, and propose an approximate algorithm PRO with an approximation ratio of (1 + ε). Moreover, we propose algorithms to optimize multiple coflows scheduling offline and online. Through extensive experiments, we demonstrate that our proposed algorithms have superior performance in reducing CCT or average CCT compared with other algorithms.
A Generative Model of Worldwide Facial Appearance

Human appearance depends on many proximate factors, including age, gender, ethnicity, and personal style choices. In this work, we model the relationship between human appearance and geographic location, which can impact these factors in complex ways. We propose GPS2Face, a dual-component generative network architecture that enables flexible facial generation with fine-grained control of latent factors. We use facial landmarks as a guide to synthesize likely faces for locations around in the world. We train our model on a large-scale dataset of geotagged faces and evaluate our proposed model, both qualitatively and quantitatively, against previous work.
GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection

Pedestrian detection is an essential task in autonomous driving research. In addition to typical color images, thermal images benefit the detection in dark environments. Hence, it is worthwhile to explore an integrated approach to take advantage of both color and thermal images simultaneously. In this paper, we propose a novel approach to fuse color and thermal sensors using deep neural networks (DNN). Current state-of-the-art DNN object detectors vary from two-stage to one-stage mechanisms. Two-stage detectors, like Faster-RCNN, achieve higher accuracy, while one-stage detectors such as Single Shot Detector (SSD) demonstrate faster performance. To balance the trade-off, especially in the consideration of autonomous driving applications, we investigate a fusion strategy to combine two SSDs on color and thermal inputs. Traditional fusion methods stack selected features from each channel and adjust their weights. In this paper, we propose two variations of novel Gated Fusion Units (GFU), that learn the combination of feature maps generated by the two SSD middle layers. Leveraging GFUs for the entire feature pyramid structure, we propose several mixed versions of both stack fusion and gated fusion. Experiments are conducted on the KAIST multispectral pedestrian detection dataset. Our Gated Fusion Double SSD (GFD-SSD) outperforms the stacked fusion and achieves the lowest miss rate in the benchmark, at an inference speed that is two times faster than Faster-RCNN based fusion networks.
A Pattern-Based Multi-Factor Authentication System

User authentication is an indispensable part of a secure system. The traditional authentication methods have been proved to be vulnerable to different types of security attacks. Artificial intelligence is being applied to crack textual passwords and even CAPTCHAs are being dismantled within few attempts. The use of graphical password as an alternate to the textual passwords for user authentication can be an efficient strategy. However, they have been proved to be susceptible to shoulder surfing like attacks. Advanced authentication systems such as biometrics are secure but require additional infrastructure for efficient implementation. This paper proposes a novel pattern-based multi-factor authentication scheme that uses a combination of text and images resulting for identifying the legitimate users. The proposed system has been mathematically analyzed and has been found to provide much larger password space as compared to simple text based passwords. This renders the proposed system secure against brute force and other dictionary based attacks. Moreover, the use of text along with the images also mitigates the risk of shoulder surfing.
Forming Future Teachers’ Communicative Competences through the Student Scientific Society Activities

Introduction. This article is devoted to the analysis of the process improving communicative qualities of graduates and future subject teachers through the integration of educational and extracurricular activities. The authors analyze main directions of implementation of interpersonal, group and organizational communication in teacher training. The purpose of the article is to substantiate the effectiveness of the program of developing communicative competences on the basis of project activities in the student scientific community by creating special diagnostic tools. Materials and Methods. As a methodological substantiation of the study, the competence approach is considered; the implementation of the latter, in conjunction with the systemic, innovation-technological, qualitative, activity-oriented and personality-oriented approaches, provides a higher qualitative level of vocational training of future teachers. At the theoretical stage of the study, the foreign and Russian pedagogical theory, practices, standards and solutions to the stated research problem were analyzed. To confirm the effectiveness of the experimental results, methods of mathematical statistical analysis were applied and a special diagnosis was created, compiled in accordance with the Professional Standard of the teacher based on content analysis of the contents of the answers, in terms of the availability of linguistic units of positive and negative connotations. The practical part of the research involved the student scientific community of the Pedagogical Institute. Results. Based on the created model for the content of the communicative competence of the future teacher, the author’s 72-hour program was developed for 6-month extra-curricular activities. It contained various forms of work: the implementation of brainstorming methods, communicative battles, a round table, essay writing, scientific stand-up, intellectual play, etc. The peculiarity of the program was that in each case, up to three roles were considered: coordinator, creative person, participant. The study presents a new perspective on the monitoring of the quality of education, in particular, the author’s diagnosis of analysis of communicative skills aimed at achieving goals of pedagogical activity . Discussion and Conclusion. Practical application of the provisions identified in the study on the integration of educational and extracurricular activities for the formation of communicative qualities of students can be realized in the organization of the educational and educational space of a higher educational institution of the pedagogical profile. The qualitative approach used in the study to analyze the labor intensity of educational disciplines in terms of counting credit units to estimate competencies can be applied as one of the indicators of monitoring basic educational programs. The study will be useful for teacher trainers wishing to improve the educational space of various learning or ganizations for this profile of training.
DPM-IEDA: Dual Probabilistic Model Assisted Interactive Estimation of Distribution Algorithm for Personalized Search

An interactive evolutionary algorithm (IEA) is powerful for solving personalized search when the user's preference can be well caught, expressed, and applied in the process of searching. Hybrid recommendation by articulating the content-based and collaborative filtering techniques is popular and effective for the personalized recommendation, but has not been developed to improve the performance of IEA for fulfilling the personalized search. Accordingly, we here propose an enhanced interactive estimation of distribution algorithm by designing dual-probabilistic models based on the hybrid recommendation for personalized search. The concept of hybrid personalized search is first defined from the viewpoint of using not only the historical search information but also the social or group preference. A dual-probabilistic model by sufficiently combining the content-based and collaborative filtering is presented and used to design the effective interactive estimation of distribution algorithm (IEDA). The probabilistic model is directly combined with the initialization of IEDA for illuminating the sparsity of the traditional IEA in encoding. The effectiveness of the proposed algorithm in fast and efficient searching with a lower computational cost is experimentally illustrated by two typical personalized searches on movies and TV series described with documents.
The performance space measurement of regional innovation system based on neuropsychology

In this paper, the performance space measurement of regional innovation system was studied based on neuropsychology. Firstly, the neuropsychology and neural evolution theory were elaborated. Secondly, the genetic algorithm was used to design a regional enterprise performance space measurement model, and this was obtained by connecting the ERP production module and RBF neural network order forecast module. Finally, the algorithm and model constructed in this paper were used to predict the performance of regional foreign trade innovation system. Then, it is concluded that the model constructed in this paper includes the network with the lowest network structure complexity, the smallest training error and the least test error. Therefore, based on this premise, a good neural network that meets the actual needs of users can be obtained, which indicates that the improved method based on evolutionary neural network is effective to measure the performance of regional innovation system.
Developing a Risk Management Framework in Construction Project Based on Agile Management Approach

Construction projects in nature, carry a lot of risks, and unpredictable conditions. Thus, flexible management is required for the purpose of efficient responding to the various changes appear during their implementation. As an attempt to deal with risk in the construction project, this research aims at proposing a risk management framework in construction projects that built based on Agile management concept, which is a sequence of procedure deals with the project’ primary vision to its final delivery. The risk management framework will trace alignment and discover a contact between Agile and traditional project management concepts and find contact points among two of the more used Agile frameworks (Scrum) and one of the more confirmed project management framework (PMBOK®) processes. This will result in a recognition of comparable areas between Scrum and PMBOK® processes. The goal of the framework is to assist the project managers to adapt a more flexible approach to managing and implementing the construction project. The results proved that Agile management process from the create prioritized project backlog, sprint planning, sprint review, to sprint retrospective procedures and less time of the cycle, eliminate or mitigate many risks that lead to project challenges and failure.
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation

An important facet of reinforcement learning (RL) has to do with how the agent goes about exploring the environment. Traditional exploration strategies typically focus on efficiency and ignore safety. However, for practical applications, ensuring safety of the agent during exploration is crucial since performing an unsafe action or reaching an unsafe state could result in irreversible damage to the agent. The main challenge of safe exploration is that characterizing the unsafe states and actions is difficult for large continuous state or action spaces and unknown environments. In this paper, we propose a novel approach to incorporate estimations of safety to guide exploration and policy search in deep reinforcement learning. By using a cost function to capture trajectory-based safety, our key idea is to formulate the state-action value function of this safety cost as a candidate Lyapunov function and extend control-theoretic results to approximate its derivative using online Gaussian Process (GP) estimation. We show how to use these statistical models to guide the agent in unknown environments to obtain high-performance control policies with provable stability certificates.
Pedestrian Detection in Visual Images Using Combination of HOG and HOM Features

The ability to detect pedestrian is an important feature in autonomous driving vehicle and advanced driver assistance system (ADAS). The detection can be very challenging due to the complex scene and bad visibility on the road. In addition, it is difficult to achieve high accuracy and good speed performance at the same time due to more processing power is required to increase the accuracy. To address this constraint, we propose a framework to detect pedestrian through fusion of image gradient and magnitude properties and the process is speed up with integral image implementation. Both image gradient and magnitude properties were extracted using Histogram of Oriented Gradient (HOG) and Histogram of Magnitude (HOM) features. From the experiment results, we showed that the combination of HOG and HOM features can achieve 99.0% accuracy compared to HOG (98.6%) or HOM (95.5%) features when they are used independently.
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model

Most of the existing learning-based single image superresolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i.e., bicubic downsampling) to their high-resolution (HR) counterparts. However, the degradations in real-world LR images are far more complicated. As a consequence, the SISR models trained on simulated data become less effective when applied to practical scenarios. In this paper, we build a real-world super-resolution (RealSR) dataset where paired LR-HR images on the same scene are captured by adjusting the focal length of a digital camera. An image registration algorithm is developed to progressively align the image pairs at different resolutions. Considering that the degradation kernels are naturally non-uniform in our dataset, we present a Laplacian pyramid based kernel prediction network (LP-KPN), which efficiently learns per-pixel kernels to recover the HR image. Our extensive experiments demonstrate that SISR models trained on our RealSR dataset deliver better visual quality with sharper edges and finer textures on real-world scenes than those trained on simulated datasets. Though our RealSR dataset is built by using only two cameras (Canon 5D3 and Nikon D810), the trained model generalizes well to other camera devices such as Sony a7II and mobile phones.