Reviewerly/exHarmony huggingface dataset:
Distillation Guided Residual Learning for Binary Convolutional Neural Networks

It is challenging to bridge the performance gap between Binary CNN (BCNN) and Floating point CNN (FCNN). We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This training strategy, i.e., optimizing each binary convolutional block with block-wise distillation loss derived from FCNN, leads to a more effective optimization to BCNN. It also motivates us to update the binary convolutional block architecture to facilitate the optimization of block-wise distillation loss. Specifically, a lightweight shortcut branch is inserted into each binary convolutional block to complement residuals at each block. Benefited from its Squeeze-and-Interaction (SI) structure, this shortcut branch introduces a fraction of parameters, e.g., 10\% overheads, but effectively complements the residuals. Extensive experiments on ImageNet demonstrate the superior performance of our method in both classification efficiency and accuracy, e.g., BCNN trained with our methods achieves the accuracy of 60.45\% on ImageNet.
How to Strengthen Mathematics Teaching in College Education

&lt;p&gt;The 21st century is an era of the coexistence of opportunities and challenges. Domestic higher education and modern science and technology have obtained unprecedented development, making people more clearly understand the vital role of mathematics, which is both pressure and power for college mathematics teaching. In addition, due attention should be taken on the problems in mathematics teaching. This paper dwells on the problems and opportunities of mathematics education in colleges and universities, hoping to find and solve the root causes of problems in the teaching process from three aspects of teachers, schools and students, so as to improve the quality and efficiency of mathematics teaching.&lt;/p&gt;
A Compromise-Based New Approach to Learning Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs), first introduced by Kosko, are graph-based knowledge representation tools. In order to improve the efficiency, robustness and accuracy of FCMs, different learning approaches and algorithms have been introduced in the literature. The algorithms aim to revise the initial knowledge of experts and/or extract useful knowledge from historical records in order to yield learned weights. One considerable drawback of FCM is that, in its original form, it often yields the same output under different initial conditions. Since the results of the learning algorithms are highly dependent on the reasoning mechanism (i.e. updating function) of FCMs, this drawback also affects the performance and accuracy of these algorithms. Therefore, problems including (conflicting) multiple initial vectors, multiple weight matrices and multiple desired final state vectors have received only limited attention. In order to address this issue and provide a better modeling framework for this type of problems, a compromise-based new fuzzy cognitive mapping approach based on particle swarm optimization is suggested. To justify the effectiveness and applicability of the proposed approach, an illustrative example is provided.
Enhancing Image Security during Transmission using Residue Number System and k-shuffle

This paper proposes an algorithm that enhances the speed of transmission and secure images that are transmitted over internet or a network. The proposed cryptosystem uses a modified k-shuffling technique to scramble pixels of images and further decomposes them using Residue Number System. Simulations are done using two moduli sets with the modified k-shuffle technique. Analyses of results showed that both simulations could secure images without any loss of information and also the time taken for a complete encryption/decryption process is dependent on the moduli set. Among the chosen moduli sets, the even moduli set optimizes and completes execution using less time as compared to the traditional moduli set. The proposed scheme also showed resistance to statistical attacks (histogram, ciphertext, correlation attacks) and a significant reduction in the size of cipher images which enhances the speed of transmission over network.
Efficient Motion Compensation and Detection Algorithm using Modified Kalman Filtering

Motion compensation and detection from video sequences are widely used in various real time applications such as security and surveillance, etc. In this paper, efficient motion compensation and detection algorithm is proposed which is mainly based upon the Modified Kalman filtering technique to detect and compensate the movement of objects present in the frames. The shadow effect is removed by thresholding operation after Blob detection. To compensate for the camera motion, the basic Kalman filter equation is modified by incorporating two dimensional motion vectors. The comparison result shows that the proposed algorithm can detect and correct movement more efficiently than existing.
Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.
DACS: Domain Adaptation via Cross-domain Mixed Sampling

Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when going from synthetic to real data. In this paper we address the problem of unsupervised domain adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of interest (target domain). Existing methods have seen success by training on pseudo-labels for these unlabelled images. Multiple techniques have been proposed to mitigate low-quality pseudo-labels arising from the domain shift, with varying degrees of success. We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo-labels. These mixed samples are then trained on, in addition to the labelled data itself. We demonstrate the effectiveness of our solution by achieving state-of-the-art results for GTA5 to Cityscapes, a common synthetic-to-real semantic segmentation benchmark for UDA.
A Novel Robust Student’s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>t</mml:mi></mml:math>-Based Cubature Information Filter with Heavy-Tailed Noises

In this paper, a novel robust Student’s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>t</mml:mi></mml:math>-based cubature information filter is proposed for a nonlinear multisensor system with heavy-tailed process and measurement noises. At first, the predictive probability density function (PDF) and the likelihood PDF are approximated as two different Student’s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>t</mml:mi></mml:math> distributions. To avoid the process uncertainty induced by the heavy-tailed process noise, the scale matrix of the predictive PDF is modeled as an inverse Wishart distribution and estimated dynamically. Then, the predictive PDF and the likelihood PDF are transformed into a hierarchical Gaussian form to obtain the approximate solution of posterior PDF. Based on the variational Bayesian approximation method, the posterior PDF is approximated iteratively by minimizing the Kullback-Leibler divergence function. Based on the posterior PDF of the auxiliary parameters, the predicted covariance and measurement noise covariance are modified. And then the information matrix and information state are updated by summing the local information contributions, which are computed based on the modified covariance. Finally, the state, scale matrix, and posterior densities are estimated after fixed point iterations. And the simulation results for a target tracking example demonstrate the superiority of the proposed filter.
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components. Such methods, however, are limited to simple transformations such as traditional data augmentation or convex combinations of two images. In this paper, we propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. Importantly, these transformations also use information from both within-class and across-class prototypical representations that we extract through clustering. We use features already computed across iterations by storing them in a memory bank, obviating the need for significant extra computation. These transformations, combined with traditional image-based augmentation, are then used as part of the consistency-based regularization loss. We demonstrate that our method is comparable to current state of art for smaller datasets (CIFAR-10 and SVHN) while being able to scale up to larger datasets such as CIFAR-100 and mini-Imagenet where we achieve significant gains over the state of art (\textit{e.g.,} absolute 17.44\% gain on mini-ImageNet). We further test our method on DomainNet, demonstrating better robustness to out-of-domain unlabeled data, and perform rigorous ablations and analysis to validate the method.
Spatial Relational Attention Using Fully Convolutional Networks for Image Caption Generation

Attention-based encoder–decoder framework has greatly improved image caption generation tasks. The attention mechanism plays a transitional role by transforming static image features into sequential captions. To generate reasonable captions, it is of great significance to detect spatial characteristics of images. In this paper, we propose a spatial relational attention approach to consider spatial positions and attributes. Image features are firstly weighted by the attention mechanism. Then they are concatenated with contextual features to form a spatial–visual tensor. The tensor is feature extracted by a fully convolutional network to produce visual concepts for the decoder network. The fully convolutional layers maintain spatial topology of images. Experiments conducted on the three benchmark datasets, namely Flickr8k, Flickr30k and MSCOCO, demonstrate the effectiveness of our proposed approach. Captions generated by the spatial relational attention method precisely capture spatial relations of objects.
Neural MOS Prediction for Synthesized Speech Using Multi-Task Learning With Spoofing Detection and Spoofing Type Classification

Several studies have proposed deep-learning-based models to predict the mean opinion score (MOS) of synthesized speech, showing the possibility of replacing human raters. However, inter- and intra-rater variability in MOSs makes it hard to ensure the high performance of the models. In this paper, we propose a multi-task learning (MTL) method to improve the performance of a MOS prediction model using the following two auxiliary tasks: spoofing detection (SD) and spoofing type classification (STC). Besides, we use the focal loss to maximize the synergy between SD and STC for MOS prediction. Experiments using the MOS evaluation results of the Voice Conversion Challenge 2018 show that proposed MTL with two auxiliary tasks improves MOS prediction. Our proposed model achieves up to 11.6% relative improvement in performance over the baseline model.
Perspectives for Mobile Learning in Higher Education in Bulgaria

The use of mobile technologies in education enables the access to and the implementation of modern teaching methods and tools. Nowadays, higher education faces a number of different challenges. Not only the technologies are developing at an extremely fast pace, but also the generations are changing—children are growing up in an environment where they are surrounded by different technological gadgets. These gadgets influence children’s communication, their access to information and also their learning habits. Upon entering the university, the learners continue to use their mobile phones for information exchange and communication. In response, higher education is trying to change in that direction. But it seems that the steps the higher education is doing are not fast enough to meet the expectations of the modern young people. The purpose of this chapter is to define the problems and to outline the perspectives for mobile learning in higher education in Bulgaria.
Estimating Emotions from Emojis and Their Use in Computer-Mediated Communication

In connection with the development of mobile devices and more frequent computer-mediated communication, most often in the form of text messages, there is a need to simply communicate the emotions associated with the communicated messages. Therefore, text messages are enriched with more information, most often in the form of emoticons, emojis or stickers. Research on emojis has been gaining popularity in the last five years. Four emojis expressing different emotions were chosen for the quantitative research presented here: joy, anger, fear, and surprise. The aim of this paper is to compare the ability to estimate emotions in a sentence and assign the correct emoji to an emotionally colored sentence in representatives of generations Y and Z in the Czech and Slovak Republics (N = 863). According to data from the conducted questionnaire, both generations are similarly successful in interpreting and using emojis in a number of situations. Yet it can be said that representatives of Generation Y were significantly more successful in recognizing joy in an emotionally colored sentence. On the other hand, representatives of Generation Z were better at connecting the correct emoji with an emotionally colored text message. There can also be found differences in the use of emojis based on gender. Women use the emoji `joy' more often than men. In an aggregated view of the success of men and women, it can be concluded that men were better able to recognize anger in an emotionally colored sentence and use this emoji correctly. On the other hand, women were better in other cases (joy, fear, and surprise). The results also indicate that the use of some types of emojis is more multifarious because emojis can be used as an additional emotional charge (information) which may not be exactly in line with the accompanying text message.
Techniques of Image Processing

In a computer vision application, images are normally ingested from their source, such as cameras, files stored on a computer disk, or streams from another application. In most cases, these input images are converted from one form into another. For example, we may need to resize, rotate, or change their colors. In some cases, we may need to remove the background pixels or merge two images. In other cases, we may need to find the boundaries around certain objects within an image.
Towards A Deeper NTRU Analysis: A Multi Modal Analysis

NTRU is being considered as part of the NIST quantum resistant cryptography standard.While NIST has received substantial attention in the literature, more analysis is needed.This current study uses a unique approach.The team of researchers divided into -sub-teams.Each is using a separate analysis technique on NTRU.Then the diverse -sub-teams' work was brought together into a single cohesive statistical analysis to provide well-founded conclusions regarding NTRU.
Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization

Temporally localizing activities within untrimmed videos has been extensively studied in recent years. Despite recent advances, existing methods for weakly-supervised temporal activity localization struggle to recognize when an activity is not occurring. To address this issue, we propose a novel method named A2CL-PT. Two triplets of the feature space are considered in our approach: one triplet is used to learn discriminative features for each activity class, and the other one is used to distinguish the features where no activity occurs (i.e. background features) from activity-related features for each video. To further improve the performance, we build our network using two parallel branches which operate in an adversarial way: the first branch localizes the most salient activities of a video and the second one finds other supplementary activities from non-localized parts of the video. Extensive experiments performed on THUMOS14 and ActivityNet datasets demonstrate that our proposed method is effective. Specifically, the average mAP of IoU thresholds from 0.1 to 0.9 on the THUMOS14 dataset is significantly improved from 27.9% to 30.0%.
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection

Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that exploit few-shot classification techniques to facilitate FSOD, this work highlights the necessity of handling the problem of scale variations, which is challenging due to the unique sample distribution. To this end, we propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD. It generates multi-scale positive samples as object pyramids and refines the prediction at various scales. We demonstrate its advantage by integrating it as an auxiliary branch to the popular architecture of Faster R-CNN with FPN, delivering a strong FSOD solution. Several experiments are conducted on PASCAL VOC and MS COCO, and the proposed approach achieves state of the art results and significantly outperforms other counterparts, which shows its effectiveness. Code is available at https://github.com/jiaxi-wu/MPSR.
Stake Shift in Major Cryptocurrencies: An Empirical Study

In the proof-of-stake (PoS) paradigm for maintaining decentralized, permissionless cryptocurrencies, Sybil attacks are prevented by basing the distribution of roles in the protocol execution on the stake distribution recorded in the ledger itself. However, for various reasons this distribution cannot be completely up-to-date, introducing a gap between the present stake distribution, which determines the parties’ current incentives, and the one used by the protocol. In this paper, we investigate this issue, and empirically quantify its effects. We survey existing provably secure PoS proposals to observe that the above time gap between the two stake distributions, which we call stake distribution lag, amounts to several days for each of these protocols. Based on this, we investigate the ledgers of four major cryptocurrencies (Bitcoin, Bitcoin Cash, Litecoin and Zcash) and compute the average stake shift (the statistical distance of the two distributions) for each value of stake distribution lag between 1 and 14 days, as well as related statistics. We also empirically quantify the sublinear growth of stake shift with the length of the considered lag interval. Finally, we turn our attention to unusual stake-shift spikes in these currencies: we observe that hard forks trigger major stake shifts and that single real-world actors, mostly exchanges, account for major stake shifts in established cryptocurrency ecosystems.
Virtual Embodiment and Embodied Cognition

Taking the perspective of others through the use of virtual reality (VR) technology increases empathy and reduces prejudice toward a myriad of social targets. The positive effects of VR perspective taking seem to be attributable to virtual embodiment and embodied cognition. For most of human history, we perceived our surroundings and functioned based on what our physical bodies were able to sense and do. Later, with the establishment of written language and symbols, we were able to expand our surroundings and learn regardless of whether or not physical bodies were present. Most of these VR systems work by replacing the perceptual input of the real world with perceptual input from the virtual environment in real time. This replacement is accomplished by: blocking out visual, auditory, and haptic feedback from the physical world; the continuous tracking of the user’s head and body movements; and the immediate rendering of virtual body and virtual environment in response to the user’s movements and behaviors.
Job matcher: A web application job placement using collaborative filtering recommender system

In today's electronic era, customized collaborative recommender systems developed for hiring and job matching process generally used to helped users identify and select qualified applicants that meet the requirements needed by any organization.Since finding an appropriate job that best fits the interest and skillsets is quite challenging for the applicants, the researcher developed a web application named "Job Matching" that provides an easy and convenient recommender application.This App is intended CICT department and its industry-partners.The researcher used the V-Model Development Model (V Model) for the system development, the researcher made it conveniently in the development by utilizing the target respondents; demonstrate the application and repetitive testing of the system to the users.The CICT Practicum Coordinator, Human Resource Representative, and CICT OJT students who are the participants and beneficiaries of the study were carefully chosen using purposive sampling.The eight criteria of the International Standard for testing and evaluation for software quality product version 25010 were used to validate the system output of the researcher.As a result, of the post-evaluation survey, the web application found to be efficient and useful as strongly agreed by the respondents.