Reviewerly/exHarmony huggingface dataset:
A study of Voice-over-Internet Protocol quality metrics

Since the pandemic began, institutions have been implementing work/study-from-home. They mostly use VoIP applications to do online meetings. This study is trying to discover what metrics are the most preferred to measure the quality of VoIP applications by reviewing 38 relevant studies. We found that the most preferred Quality of Experience (QoE) metrics are Mean Opinion Score (MOS), Perceptual Evaluation of Speech Quality (PESQ), and Peak Signal-to-noise Ratio (PSNR). While the most preferred Quality of Service (QoS) metrics are jitter, bandwidth, packet loss, and throughput. We also found that there are not many new studies done on evaluating SIP quality.
Dynamic User Interface Personalization Based on Deep Reinforcement Learning

Personalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its' ability to learn actions the way humans learn from experience, if utilized and evaluated properly it can result in a revolutionary effect on personalization. The methodology proposed in this research utilizes deep reinforcement learning where an artificial agent may be trained by interacting with its environment. Utilizing the experience gathered, the agent is able optimize in the form of rewards. The approach explained, can be utilized across applications which can be personalized. Several scenarios ranging from changing the layout of webpages, to rearranging icons on mobile home screens are discussed. The main objective is to develop an API for the web developers and smartphone manufacturers to utilize so that depending on the application personalization can be achieved by enhancing saliency, minimizing selection time, increasing engagement, or an arrangement of these. The technique can manage a variety of adaptations, such as how graphical elements are shown and how they behave. An experiment was conducted which showcased improved user experience considering the position change of the user interface elements thereby personalizing the layout.
Relation-Aware Attribute Network for Fine-Grained Clothing Recognition

In recent years, clothing attribute recognition made significant progress in the development of fine-grained clothing datasets. However, most existing methods treat some related attributes as different categories for attribute recognition in these fine-grained datasets, which ignores the intrinsic relations between clothing attributes. To describe the relations between clothing attributes and quantify the influence of attribute relation on attribute recognition tasks, we propose a novel Relation-Aware Attribute Network (RAAN). The relations between clothing attributes can be characterized by the Relation Graph Attention Network (RGAT) constructed for each attribute. Moreover, with the combination of visual features and relational features of attribute values, the influence of attribute relations on the attribute recognition task can be quantified. Extensive experiments show the effectiveness of RAAN in clothing attribute recognition.
The Causal Link Between Savings and Economic Growth in Bosnia and Herzegovina

Abstract The causal link between savings and economic growth has been extensively discussed in the economic growth and development literature, but the question of the direction of this link has not yet been clearly defined. The aim of this paper is to determine the direction and intensity of savings causality (components of private savings) and economic growth in Bosnia and Herzegovina. Granger’s causality test, the Toda-Yamamoto procedure, was applied to test for causality between savings and economic growth. The results of Granger’s causality test indicated that there is no causal link between components of private savings with economic growth in Bosnia and Herzegovina. It was found that among the variables there is cointegration, but not causality, which means that the variables have a common stochastic trend. This fully corresponds to the characteristics of the Bosnia and Herzegovina’s economy.
A remote ethnography methodology to gain packaging behaviour insights

Abstract Remote ethnography requires the observation of human interaction in the natural world without the researcher being present. Well‐established in industrial and user experience design remote ethnography provides insight into the user's behaviour whilst completing a specific task in a defined environment. Designers in established fields such as Design for Sustainable Behaviour have applied this behavioural understanding to develop interventions to positively adapt unsustainable behaviours. Existing research techniques have evidenced limitations in fully understanding consumer packaging disposal behaviour, with a clear gap in behavioural insights with packaging used out of the home. A novel mixed‐methods approach was developed using remote ethnography to explore consumer food‐to‐go packaging disposal behaviour out of the home, providing insights which could be evaluated for their application within the packaging development process. In explaining the new methodological approach, this paper (a) proposes a mixed‐methods approach by which packaging developers can better understand packaging disposal behaviour out of the home, (b) explains this research method in the context of a food‐to‐go packaging disposal case study and (c) evaluates the value of the mixed‐methods approach within the food packaging development process.
WiP: QoS Based Recommendation System for Efficient Private Inference of CNN Using FHE

Convolutional Neural networks have been widely adopted in computer vision because of their robust performance in a variety of applications. Many of the image detection and classification algorithms are being offered as a service by the Cloud service providers. In its current manifestation, the service provider has access to the rich data that is sent as a query thereby compromising the privacy of the user. Encryption can be used to ensure security during transmission. However, it cannot ensure data privacy. In order to protect the privacy and ensure security of the user data, there is a need to develop new approaches that can perform computer vision tasks on encrypted data. In this paper, Fully Homomorphic Encryption (FHE) is used to ensure security and privacy of the data. The proposed method builds the necessary algorithms to allow the server to make inferences on the encrypted input and give encrypted result back to the user. Deep neural network for FHE encrypted data is computationally very heavy. In order to address this problem, variable length packing on the pruned deep learning model is employed. An algorithm to automatically recommend appropriate parameters for pruning and variable length packing is proposed. Further, user’s preference for Quality of Service (QoS) is taken into account in the proposed framework. CIFAR-10 image dataset is used to evaluate the method in terms of performance and accuracy for a ten class classification. Our experimental analysis show upto 60% improvement in terms of performance while using our optimizations.
Target Tracking Based on Particle Filter and Mean Shift

Robustness and real time are just two pursuing goals in visual tracking area. The main advantage of particle filters is its robust performance, but it can't be applied in critical real time requirement fields due to its long time consuming. Although mean shift algorithm have the advantage of real time, tracking drifts will occur if the video contain complex scenarios, that is to say, the disadvantage of mean shift algorithm is its poor robustness. Infrared target tracking algorithm used in imaging guidance system must have the advantage of both robustness and real time. Aimed at the fact, a novel tracking method tightly coupling particle filter and mean shift algorithm is proposed in this paper. Compared some existing tracking algorithm using both particle filters and mean shift, the tight coupling in the paper means that a five parameter set (2-D central location, width, height, and orientation) is utilized to fully describe a possible state of target, and the parameter set not only works as a particle in the particle filter algorithm, but also can be revised by the mean shift algorithm, other than the 2-D central location only. Experimental results show that the proposed tracking method is robust and in real time, even if videos contain complex scenarios.
Accelerating Density-Based Subspace Clustering in High-Dimensional Data

Subspace clustering aims to find all clusters in all subspaces of a high-dimensional data space. We present a massively data-parallel approach that can be run on graphics processing units. It extends a previous density-based method that scales well with the number of dimensions. Its main computational bottleneck consists of (sequentially) generating a large number of minimal cluster candidates in each dimension and using hash collisions in order to find matches of such candidates across multiple dimensions. Our approach parallelizes this process by removing previous interdependencies between consecutive steps in the sequential generation process and by applying a very efficient parallel hashing scheme optimized for GPUs. This massive parallelization gives up to 70x speedup for the bottleneck computation when it is replaced by our approach and run on current GPU hardware. We note that depending on data size and choice of parameters, the parallelized part of the algorithm can take different percentages of the overall runtime of the clustering process, and thus, the overall clustering speedup may vary significantly between different cases. However, even in our "worst-case" test, a small dataset where the computation makes up only a small fraction of the overall clustering time, our parallel approach still yields a speedup of more than 3x for the complete run of the clustering process. Our method could also be combined with parallelization of other parts of the clustering algorithm, with an even higher potential gain in processing speed.
The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models

In recent years a lot of research was conducted within the area of causal inference and causal learning. Many methods were developed to identify the cause-effect pairs. These methods also proved their ability to successfully determine the direction of causal relationships from observational real-world data. Yet in bivariate situations, causal discovery problems remain challenging. A class of methods, that also allows tackling the bivariate case, is based on Additive Noise Models (ANMs). Unfortunately, one aspect of these methods has not received much attention until now: what is the impact of different noise levels on the ability of these methods to identify the direction of the causal relationship? This work aims to bridge this gap with the help of an empirical study. We consider a bivariate case and two specific methods Regression with Subsequent Independence Test and Identification using Conditional Variances. We perform a set of experiments with an exhaustive range of ANMs where the additive noises’ levels gradually change from 1% to 10000% of the causes’ noise level (the latter remains fixed). Additionally, we consider several different types of distributions as well as linear and non-linear ANMs. The results of the experiments show that these causal discovery methods can fail to capture the true causal direction for some levels of noise.
Endoscopic Ultrasound Image Recognition Based on Data Mining and Deep Learning

The recognition of medical images, especially endoscopic ultrasound images, has the characteristics of changing images and insignificant gray-scale changes, which requires repeated observation and comparison by medical staff. In view of the above-mentioned characteristics of ultrasound imaging, a system scheme suitable for image processing is proposed, which can analyze the biliary tract, gallbladder, abdominal lymph nodes, liver, descending duodenum, duodenal bulb, stomach, pancreas, pancreatic lymph nodes, there are a total of 10 ultrasonic organs, including 21 kinds of sub-categories and 3510 images. The images are preprocessed using binarization, histogram equalization, median filtering and edge enhancement algorithms. The improved YoloV4 convolutional neural network algorithm is used to train the data set and perform high accuracy is detected in real time. Finally, the average accuracy of this algorithm has reached 91.59%. The algorithm proposed in this paper can make up for the shortcomings of manual detection in the original image detection system, improve the efficiency of detection, and at the same time as an auxiliary system can reduce detection misjudgments, and promote the development of automated and intelligent detection in the medical field.
User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient manner.The advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation (RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any user.Due to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient service.To improve the performance in data security, the block chains are used for securing the data in the cloud environment.However, the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the cloud.In this paper, an efficient user centric block level Attribute Based Encryption (UCBL-ABE) scheme is presented to provide the efficient security of cloud data in cloud environment.The proposed approach performs data transaction by employing the block chain.The proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust (DLAT).Based on DLAT, the user access has been restricted in the cloud environment.The proposed protocol is implemented in real time using Java programming language and uses IBM cloud.The implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.
A Blockchain Based PKI System for Peer to Peer Network

The Public Key Infrastructure (PKI) technology is the core part of authenticating users and devices in a network. PKI enables secure communication between users and devices. The certification authority (CA) in PKI is responsible for issuing the key pair and digital certificate. The trust of PKI stands on CA but nowadays many articles are reported such as DDoS attack to Digicert Sdn (Axon in Privacy-awareness in blockchain-based pki, 2015) resulting fake certificates issued by CA. On the other hand, the traditional PKI system is pruned to single point failure where the corruption of CA causes failure of the whole network. The blockchain based PKI removes the barrier present in the traditional PKI system by decentralizing the certificate authority. This will enable the interested nodes to generate their own key pair private key prkey and public key pbkey. In this research paper, we have proposed a block chain based PKI system for issuing and validation the key pairs and certificates for a peer to peer (P2P) network. The proposed system is implemented in Geth an open Blockchain environment and the smart contract has been deployed in solidity language.
Computational Pronunciation Analysis in Sung Utterances

Recent automatic lyrics transcription (ALT) approaches focus on building stronger acoustic models or in-domain language models, while the pronunciation aspect is seldom touched upon. This paper applies a novel computational analysis on the pronunciation variances in sung utterances and further proposes a new pronunciation model adapted for singing. The singing-adapted model is tested on multiple public datasets via word recognition experiments. It performs better than the standard speech dictionary in all settings reporting the best results on ALT in a capella recordings using n-gram language models. For reproducibility, we share the sentence-level annotations used in testing, providing a new benchmark evaluation set for ALT.
Logit-based Uncertainty Measure in Classification

We introduce a new, reliable, and agnostic uncertainty measure for classification tasks called logit uncertainty. It is based on logit outputs of neural networks. We in particular show that this new uncertainty measure yields a superior performance compared to existing uncertainty measures on different tasks, including out of sample detection and finding erroneous predictions. We analyze theoretical foundations of the measure and explore a relationship with high density regions. We also demonstrate how to test uncertainty using intermediate outputs in training of generative adversarial networks. We propose two potential ways to utilize logit-based uncertainty in real world applications, and show that the uncertainty measure outperforms.
Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction

Crime prediction models seek to assist policymakers and law enforcement agencies in the allocation of scarce resources intended to prevent crime occurrences. This paper proposes an extension and two interpretation methods for a novel conditional GANs architecture for crime (robberies) prediction in Bogota, Colombia. The model’s performance on the area under the Hit Rate - Percentage Area Covered by Hotspots curve increases from 0.86 to 0.88 AUC when extended by conditioning on holidays. The proposed interpretability methods can help study the effect of crime occurring in a region on the likelihood of occurrence in other regions through the use of SHAP values. These interpretations can prove to be very useful for policymakers and law enforcement agencies in designing interventions and preventing future crime from inferring potential regions of displacement. (Results of the project “Diseño y validación de modelos de analítica predictiva para la toma de decisiones en Bogotá” funded by Colciencias with resources from the Sistema General de Regalías, BPIN 2016000100036. The opinions expressed are solely those of the authors .)
Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data

Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training set, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require learning algorithms that prevent the propagation of known bias from the dataset into the classifier. We present a novel adversarial debiasing method, which addresses a feature of which we know that it is spuriously connected to the labels of training images but statistically independent of the labels for test images. The debiasing stops the classifier from falsly identifying this irrelevant feature as important. Irrelevant features co-occur with important features in a wide range of bias-related problems for many computer vision tasks, such as automatic skin cancer detection or driver assistance. We argue by a mathematical proof that our approach is superior to existing techniques for the abovementioned bias. Our experiments show that our approach performs better than the state-of-the-art on a well-known benchmark dataset with real-world images of cats and dogs.
Deep lifted decision rules for two-stage adaptive optimization problems

This paper presents a novel method to generate flexible piecewise linear decision rules for two-stage adaptive optimization problems. Borrowing the idea of a neural network, the lifting network consists of multiple processing layers that enable the construction of more flexible piecewise linear functions used in decision rules whose quality and flexibility is superior to linear decision rules and axially-lifted ones. Two solution methods are proposed to optimize the weights and the decision rule approximation quality: a derivative-free method via an evolutionary algorithm and a derivative-based method using approximate derivative information. For the latter method, we suggest local-search heuristics that scale well and reduce the computational time by several folds while offering similar solution quality. We illustrate the flexibility of the proposed method in comparison to linear and axial piecewise linear decision rules via a transportation and an airlift operations scheduling problem.
Deep Neural Language-agnostic Multi-task Text Classifier

Many publications prove that the creation of a multiobjective machine learning model is possible and reasonable. Moreover, we can see significant gains in expanding the knowledge domain, increasing prediction quality, and reducing the inference time. New developments in cross-lingual knowledge transfer open up a range of possibilities, particularly in working with low-resource languages. With a motivation to explore the latest subfields of natural language processing and their interactions, we decided to create a multi-task multilingual model for the following text classification tasks: functional style, domain, readability, and sentiment. The paper discusses the effectiveness of particular language-agnostic approaches to Polish and English and the effectiveness and validity of the multi-task model.
Limitations on freedom of expression in practice of the European Court of Human Rights and the notion of disinformation

The phenomenon of disinformation spreading is a well-known phenomenon. The problem has intensified primarily due to advances in technology. In addition, there is an increased desire for additional information in circumstances of uncertainty and crisis. Some states even resorted to vague or too strict laws or derogations of rights in case of public emergency. While examining whether there is a need for interference with freedom of expression, it seems that the European Court of Human Rights does not give special relevance to the term of disinformation. Nevertheless, two key elements of disinformation can be distinguished. Disinformation is tied to the information's veracity and the intent of the information provider. When determining whether an interference with freedom of expression is justified, the European Court of Human Rights gives these elements some weight, along with comprehensive analysis of other relevant factors. Fear from spreading disinformation must not lead the authorities to simply adopt strict and/or vague laws with severe penalties, which might fail to meet the requirements of freedom of expression protection. However, the term of disinformation can have value in policymaking directed at raising the credibility of information in general.