Reviewerly/exHarmony huggingface dataset:
Influence of Gender and Viewing Frequency on Quality of Experience

Some of the most important aspects for content creators and service providers are the content appeal and the time consumed by the end users on a particular application or service. Gender and age can influence the Quality of Experience (QoE) ratings of multimedia based on the nature of the shown content, yet few studies have quantized this notion. In this paper, we zoom in on the influence of gender on user ratings in a video QoE study (N=89) with packet loss as the main system influence factor. We have analyzed the impact of gender on QoE subjective ratings both as a standalone influence factor and in coherence of temporal traits like the frequency of watching online content. We have observed significant trends to highlight the importance of systematically checking and reporting on the impact of basic human factors, such as gender in relation to quality perception with respect to different types of content.
Blur detection via deep pyramid network with recurrent distinction enhanced modules

Blur detection aims to detect the regions where the image is blur and identifies the blurred regions accurately. Since the traditional hand-crafted feature based methods usually are not robust enough to handle various complex scenarios, the study of blur detection problem remains to be a challenging task in terms of the accuracy and effectiveness of blur separation. In this paper, instead of seeking a new hand-crafted feature often seen in common approaches, we present a new deep feature-learning method by using feature pyramid network via recurrent Distinction Enhanced Block modules to solve the blur detection problem. We show that the newly introduced Distinction Enhanced Block is able to merge the high-level semantic information with the low-level details effectively, while at the same time, it is capable to keep a desirable identification between the blurry and clear regions. We also develop a new boundary penalty term to refine the boundaries of our results, which leads to more accurate detection and segmentation, compared with current available results in literature. Furthermore, due to the lacking of public datasets for blur detection problems, we have established a new blur detection dataset which may enrich the experimental benchmarks for testing. In terms of efficiency associated with the competitive performance, in particular, our model is shown to have best-in-class evaluation speed, about 0.14 s to evaluate an input image in regular personal laptop. The performance evaluations on both CUHK dataset and our SZU-BD dataset validate that the proposed model outperforms various existing state-of-the-arts methods through commonly used metric indexes.
Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario

Deep Learning-based (DL) image compression has shown prominent results compared to standard image compression techniques like JPEG, JPEG2000, BPG and WebP. Nevertheless, neither DL nor standard techniques generally can cope with critical real-world scenarios, with stringent performance constraints. In order to explore the nature of this gap, we first introduce an industrial scenario, which contemplates real-time compression of high-resolution images, with strict requirements on a number of quality-performance indicators, namely: the output image quality, the hardware, and the compression complexity. Next, we propose a DL-based image compression model, i.e. a Convolutional Residual Autoencoder (CRAE). In particular, CRAE integrates some structural benefits of a deep neural network, including PReLU activation function and sub-pixel convolution, which have proven to be especially suitable for image compression tasks. We analyze the performance of the proposed CRAE approach by adopting two types of processing: (i) global and, (ii) patch-based processing of image data. To test the models, we exploit a dataset composed of high-resolution images provided by the MERMEC company composed of consecutive images of the railway track captured by a machine vision system called V-CUBE. Furthermore, the company provided strict compression requirements that needed to be met by the developed system. Preliminary results of an ongoing study indicates that the proposed image compression system can meet the requirements by MERMEC with reasonable performance, with a mild advantage observed for full-based CRAE. The obtained outcomes suggests that CRAE can adapt to the specific structure of the given dataset and extracts the salient recurrent patterns inside an image. In summary, this line of research represents the core of the future plug-and-play DL architecture for constrained image compression.
Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.
Logarithmic-Size (Linkable) Threshold Ring Signatures in the Plain Model

A 1-out-of-N ring signature scheme, introduced by Rivest, Shamir, and Tauman-Kalai (ASIACRYPT '01), allows a signer to sign a message as part of a set of size N (the so-called "ring") which are anonymous to any verifier, including other members of the ring. Threshold ring (or "thring") signatures generalize ring signatures to t-out-of-N parties, with $$t \ge 1$$ , who anonymously sign messages and show that they are distinct signers (Bresson et al., CRYPTO'02). Until recently, there was no construction of ring signatures that both (i) had logarithmic signature size in N, and (ii) was secure in the plain model. The work of Backes et al. (EUROCRYPT'19) resolved both these issues. However, threshold ring signatures have their own particular problem: with a threshold $$t \ge 1$$ , signers must often reveal their identities to the other signers as part of the signing process. This is an issue in situations where a ring member has something controversial to sign; he may feel uncomfortable requesting that other members join the threshold, as this reveals his identity. Building on the Backes et al. template, in this work we present the first construction of a thring signature that is logarithmic-sized in N, in the plain model, and does not require signers to interact with each other to produce the thring signature. We also present a linkable counterpart to our construction, which supports a fine-grained control of linkability. Moreover, our thring signatures can easily be adapted to achieve the recent notions of claimability and repudiability (Park and Sealfon, CRYPTO'19).
Towards Meta-interpretive Learning of Programming Language Semantics

We introduce a new application for inductive logic programming: learning the semantics of programming languages from example evaluations. In this short paper, we explore a simplified task in this domain using the Metagol meta-interpretive learning system. We highlight the challenging aspects of this scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and propose extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.
On training a classifier of hitting times for quantum walks

We study hitting times of quantum walks on graphs from a machine learning perspective. Given a graph it is difficult to decide if quantum walks give an advantage relative to classical random walks. It was shown that machine learning can help to detect quantum advantage even though quantum walk and random walk dynamics on the graph has never been simulated. Here we describe the procedure by which training data is generated for the machine learning approach, and discuss possible future improvements.
Election Technology, Disputes, and Political Violence in Kenya

In Africa, many countries have adopted technology in the conduct of elections so as to improve efficiency and deter malpractice. However, electoral disputes and violence continue to recur even where elections involve use of technology. This article builds on a case study of Kenya to show the limitations of technology use in elections. We argue that use of election technology does not guarantee the credibility of elections; technology has become a ‘black box’, which competing parties exploit to either play victim or declare themselves winners. The paper concludes that election technology is the new frontier for fraud. The manner in which election technology is procured, deployed and utilized is not only technical but also political. Addressing this political problem is imperative to avoid political violence around elections.
A ConvNet Based Procedure for Image Copy-Move Forgery Detection

Copy-Move forgery is one of the popular image tempering procedure. In which the forger modifies the original image by creating multiple instances of some objects within the image itself. Recently, several deep convnet methods have been applied in the classification of images, forensic images, image hashing retrieval, and so on, showing better performance than the traditional method. In this paper, a new architecture of deep learning is proposed for image copy-move forgery detection. This architecture includes VGG16 as the first layer, then RPN for proposing a set of regions and an “object” score for each region and RoI for finding interested area. Both original and forged area in this method is being localized. Through intensive experiments on multiple datasets, we demonstrate that the proposed model is very effective and robust against a number of known attacks. Which outperforms other state-of-the-art image copy-move forgery detection approaches.
Metric Learning with A-based Scalar Product for Image-set Recognition

In this paper, we propose a metric learning method for image set recognition using subspace representation. The subspace representation is effective for image set recognition where each image set is compactly represented by a subspace in a high dimensional vector space. In this framework, the similarity between two given image sets is measured by the canonical angles between the two corresponding subspaces. Many types of methods utilizing the concept of canonical angles have been developed and studied extensively. However, there still remains large potential in improving the ability to measure canonical angles. Our key idea is to learn a general scalar product space (metric space) that produces more valid canonical angles between two subspaces. To realize this idea, we first introduce an A-based scalar product instead of the standard scalar product, where A is a symmetric positive definite matrix and the canonical angles between two subspaces are measured through the A-based scalar product. We learn a discriminative metric space by optimizing metric A in terms of the Fisher ratio from local Fisher discriminant analysis. Besides, we introduce a mechanism to automatically reduce the dimension of the metric space by imposing a low-rank constraint on metric A. The effectiveness of the proposed methods is validated through extensive classification experiments on three real-world datasets.
Levy Flight Opposition Embedded BAT Algorithm for Model Order Reduction

The analytical study of large-scale linear time-invariant systems is a very tedious and complicated task in a category of real-life optimization problems. So, simplification procedures for these complex problems are needed. In the solution tactic of this complex problem, Model Order Reduction (MOR) is a novel concept providing a simpler model than the original one based on mathematical approximation. In literature, several meta-heuristics are employed to solve MOR problem. In the same line of order, this chapter presents a technique to solve MOR problem using modified BAT algorithm based on levy flight and opposition based learning. The concept of Levy flight random walk and opposition based learning (OBL) is embedded to BAT algorithm (BA) to avoid local optima trapping and to enhance the exploitation and exploration ability. To evaluate the performance of the proposed methodology, it is tested over three different MOR problems of different transfer functions. The numerical and statistical results confirmed the supremacy of the proposed variant in terms of stability of reduced order systems.
Determination of Positional Characteristics of Numbers in the Residual Class System

The article explores techniques for implementing arithmetic operations in the system of residual classes (SRC). Examples of determination of positional characteristics of numbers which are presented in the residual class system are given. In particular, the determination of the rank of a number can be carried out in the dynamics of a computational process, i.e. without stopping calculations for the duration of non-positional operations of transferring numbers from the system of residual classes to the positional number system and vice versa. The method of determination of positional characteristic of non-positional code – rank of number in SRC is considered in the article. Examples are given to show that determination of the rank of number can be implemented without stopping the calculation process. This gives the opportunity to fully use the main feature of the RC, which is high-speed of performing arithmetic operations.
The Study For Path Following of Robot Fish Based on ADRC

The multi-joint robot fish is often subject to unknown external and internal disturbances during path following in water areas. In this paper, a path following control method based on active disturbance rejection control (ADRC) is proposed for obtaining high-quality tracking effect. Combining the kinematics and dynamics equations of the three-joint robot fish, the model of error for path following is established in the Serret-Frenet coordinate system by setting the virtual mobile robot. Based on the introduction of expectation angle, the guidance of forwarding and steering control law are designed respectively. Then the second-order path following controller based on ADRC is established to improve the robustness and rapidity of the robot fish in the path following. For the control of such systems, a disturbance observer and related techniques have provided a powerful tool to dynamically estimate and compensated the diverse disturbances and offered desired control performances. The experiment shows that the method proposed in this paper enables the robot fish to follow the given path at around 3s, and the error of position maintained at positive or negative 0.1m. Compared with the conventional PID control method, the results show that path following controller based the ADRC can restrain overshoot very well and follow the given path quickly, resulting in an improvement of path tracking time and accuracy.
Webometrics ranking analysis and possible ways to improve the position of the university

In the article is examined the Webometrics system, which twice a year makes a ranking of universities around the world. Criteria are given and with what weight these indicators influence the position in the rating list is indicated. The methods and four evaluation criteria by which a conducted ranking were considered. The problem of low positions of Ukrainian universities in the list of world rankings is formulated. The general solutions for increasing the university rating are presented. It was established that in order to increase the rating of a particular university, it is necessary to develop an individual strategy for promoting the university in the ranking. Namely, build a Webometrics prediction estimation model, implement the function of introspection.
Zero-based serials review: An objective, comprehensive method of selecting full-text journal resources in response to local needs

Most serials reviews have focused on the cancellation of currently held resources with high cost per use. In contrast, the latest serials review at Manhattan College employed a more fundamental indicator of cost-effectiveness and did not use the library's initial holdings as a starting point. After compiling a list of 2717 wanted journals—those identified by departmental faculty—we evaluated several thousand full-text journal resources (online databases and single-journal subscriptions) to identify those that provide the most cost-effective access to the wanted journals. Our iterative process led to a 50% increase in the percentage of wanted journals held, a 35% reduction in cost per wanted journal, a 3% reduction in total expenditures on full-text journal resources, and a substantial reduction in inequality of holdings across academic departments. Because costs are low in the initial rounds of the process, the same general method can be used to reduce expenditures. Two-thirds of the selected journals could have been acquired for just one-third of the total expenditure, and 80% could have been acquired for just 53%. Our results demonstrate that objective methods based on cost per wanted journal can achieve good results, and that commercial publishers' databases can be central to a cost-effective journal collection.
Multi-party Revocation in Sovrin: Performance through Distributed Trust

Accumulators provide compact representations of large sets and compact membership witnesses. Besides constant-size witnesses, public-key accumulators provide efficient updates of both the accumulator itself and the witness. However, bilinear group based accumulators come with drawbacks: they require a trusted setup and their performance is not practical for real-world applications with large sets.
Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity

As an online, crowd-sourced, open English-language slang dictionary, the Urban Dictionary platform contains a wealth of opinions, jokes, and definitions of terms, phrases, acronyms, and more. However, it is unclear exactly how activity on this platform relates to larger conversations happening elsewhere on the web, such as discussions on larger, more popular social media platforms. In this research, we study the temporal activity trends on Urban Dictionary and provide the first analysis of how this activity relates to content being discussed on a major social network: Twitter. By collecting the whole of Urban Dictionary, as well as a large sample of tweets over seven years, we explore the connections between the words and phrases that are defined and searched for on Urban Dictionary and the content that is talked about on Twitter. Through a series of cross-correlation calculations, we identify cases in which Urban Dictionary activity closely reflects the larger conversation happening on Twitter. Then, we analyze the types of terms that have a stronger connection to discussions on Twitter, finding that Urban Dictionary activity that is positively correlated with Twitter is centered around terms related to memes, popular public figures, and offline events. Finally, We explore the relationship between periods of time when terms are trending on Twitter and the corresponding activity on Urban Dictionary, revealing that new definitions are more likely to be added to Urban Dictionary for terms that are currently trending on Twitter.
Introduction to the Special Issue on Deep Learning for Multi-Modal Intelligence Across Speech, Language, Vision, and Heterogeneous Signals

The ten papers included in this special section focus on deep learning for multi-modal intelligence across speech, language, vision, and heterogeneous signals. Thanks to the disruptive advances in deep learning, significant progress has been made in artificial intelligence (AI) applications with single modality, such as speech recognition, speech synthesis, image classification, object detection, as well as machine translation and reading comprehension, etc. However, many AI problems require more than one modality, and techniques developed for different modalities can often be successfully cross-fertilized. Therefore, the studies on the modeling and learning approaches across multiple modalities are of great interest. This special issue brings together a diverse but complementary set of contributions on emerging deep learning methods for problems based on multiple modalities including speech, text, image and video.
Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier

Being able to recognize human activities is essential for several applications such as health monitoring, fall detection, context-aware mobile applications. In this work, we perform the recognition of the human activity based on the combined Weighted SVM and HMM by taking advantage of the relative strengths of these two classification paradigms. One significant advantage in WSVMs is that, they deal the problem of imbalanced data but his drawback is that, they are inherently static classifiers - they do not implicitly model temporal evolution of data. HMMs have the advantage of being able to handle dynamic data with certain assumptions about stationary and independence. The experiment results on real datasets show that the proposed method possess the better robustness and distinction.
Convolutional Neural Network with Squeeze and Excitation Modules for Image Blind Deblurring

Image blind deblurring is a classic computer vision and image processing task. It has been proved that end-to-end deep convolutional neural network (CNN) has the highest performance on nonuniform motion blind deblurring task from dynamic scene. Recently, many well designed multi-scale CNNs have been established. These CNNs process blurry images at different image resolution and improve the quality of restored image obviously. In this paper, inspired by Squeeze and Excitation Network (SENet), we introduce the core module named Squeeze and Excitation module (SE module) of SENet into our network to deblur the blurry images. We compare our network with three state-of-the-art methods on two standard datasets. Experiment results show that our proposed method outperforms other state-of-the-art methods quantitatively and visually.