Reviewerly/exHarmony huggingface dataset:
Second-Order Asymptotically Optimal Outlier Hypothesis Testing

We revisit the outlier hypothesis testing framework of Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (TIT 2014) and derive fundamental limits for the optimal test under the generalized Neyman-Pearson criterion. In outlier hypothesis testing, one is given multiple observed sequences, where most sequences are generated i.i.d. from a nominal distribution. The task is to discern the set of outlying sequences that are generated from anomalous distributions. The nominal and anomalous distributions are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown</i> . We study the tradeoff among the probabilities of misclassification error, false alarm and false reject for tests that satisfy weak conditions on the rate of decrease of these error probabilities as a function of sequence length. Specifically, we propose a threshold-based test that ensures exponential decay of misclassification error and false alarm probabilities. We study two constraints on the false reject probability, with one constraint being that it is a non-vanishing constant and the other being that it has an exponential decay rate. For both cases, we characterize bounds on the false reject probability, as a function of the threshold, for each pair of nominal and anomalous distributions and demonstrate the optimality of our test under the generalized Neyman-Pearson criterion. We first consider the case of at most one outlying sequence and then generalize our results to the case of multiple outlying sequences where the number of outlying sequences is unknown and each outlying sequence can follow a different anomalous distribution.
A machine learning approach for detecting fast flux phishing hostnames

Attackers are increasingly using Fast Flux Service Networks (FFSNs), networks of compromised machines, to host phishing websites. In FFSNs, the machines rapidly change such that blacklisting them does not entirely stop the networks from operating the websites. This increases the longevity of the websites thus becoming more harmful. Existing solutions for detecting the websites are limited with relatively low or moderate prediction performances, high prediction time and use of less diversified features which increases their susceptibility to detection evasions. This paper proposes a Machine Learning (ML) based approach for detecting phishing websites hosted in FFSNs using a novel set of 56 features. Compared with previous works, the approach achieves high accuracy, a low detection time and uses highly diversified features to enhance resilience to detection evasion. The effectiveness of the features for prediction was evaluated in the context of binary and multi-class classification tasks using multiple traditional and deep learning ML algorithms. The proposed approach achieves an accuracy of 98.42% and 97.81% for binary and multi-class classification tasks respectively. Our results showed that temporal and DNS based features are the strongest predictors while network and host related features are the weakest. Our approach is a significant step towards tracking of core components of FFSNs with an aim of shutting down the entire phishing ecosystem.
Understanding Roxygen package documentation in R

R is a package-based programming ecosystem that provides an easy way to install third-party code, datasets, and examples. Thus, R developers rely heavily on the documentation of the packages they import to use them correctly and accurately. This documentation is often written using Roxygen, equivalent to Java’s well-known Javadoc. This two-part study provides the first analysis in this area. First, 379 systematically-selected, open-source R packages were mined and analysed to address the quality of their documentation in terms of presence, distribution, and completeness to identify potential sources of documentation debt of technical debt that describes problems in the documentation. Second, a survey addressed how R package developers perceive documentation and face its challenges (with a response rate of 10.04%). Results show that incomplete documentation is the most common smell, with several cases of incorrect use of the Roxygen utilities. Unlike in traditional API documentation, developers do not focus on how behaviour is implemented but on common use cases and parameter documentation. Respondents considered the examples section the most useful, and commonly perceived challenges were unexplained examples, ambiguity, incompleteness and fragmented information.
Teaching for Values in Human–Computer Interaction

There is an increasing awareness of the importance of considering values in the design of technology. There are several research approaches focused on this, such as e.g., value-sensitive design, value-centred human–computer interaction (HCI), and value-led participatory design, just to mention a few. However, less attention has been given to developing educational materials for the role that values play in HCI, why hands-on teaching activities are insufficient, and especially teaching activities that cover the full design process. In this article, we claim that teaching for ethics and values in HCI is not only important in some parts of the design and development process, but equally important all through. We will demonstrate this by a unique collection of 28 challenges identified throughout the design process, accompanied by inspirational suggestions for teaching activities to tackle these challenges. The article is based on results from applying a modified pedagogical design pattern approach in the iterative development of an open educational resource containing teaching and assessment activities and pedagogical framework, and from pilot testing. Preliminary results from pilots of parts of the teaching activities indicate that student participants experience achieving knowledge about how to understand and act ethically on human values in design, and teachers experience an increased capacity to teach for values in design in relevant and innovative ways. Hopefully, this overview of challenges and inspirational teaching activities focused on values in the design of technology can be one way to provide teachers with inspiration to sensitize their students and make them better prepared to become responsible designers by learning how to address and work with values in HCI.
Exploring the effect of emotions in human–machine dialog: An approach toward integration of emotional and rational information

The most important issues in developing an emotion-aware dialog system include how to recognize users' emotions and how machine responses can be generated accordingly. However, studies on emotional dialogs have primarily focused on whether the system can produce utterances with suitable emotions; consequently, the content relevance and quality of the system's responses have been ignored. Different from previous studies, we present a deep learning framework to explore the influence of emotions in dialogs. Our framework includes two modules (C-LSTM for emotion recognition and biLSTM-C for response generation), and it integrates both emotional and rational information to produce emotionally and semantically correct responses. This structured design has several advantages: the modules of emotion recognition and response generation can be constructed by any effective methods, the personalized dialog can be achieved by adjusting the emotion–response coupling mechanism to adapt to users' conversational styles, and the results can be transparent and interpretable to users. Following the presented approach, we first assess the performance of model training for emotion recognition and response generation. With the learned models, we configure a series of experiments to investigate the effect of using emotion as a driving force to generate machine responses in length by designing evaluation strategies from different perspectives. Experiments and results highlight the importance and influence of emotions in dialogs.
Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization

Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resource utilization and cost optimization in the cloud. CMRBC has two benefits. Firstly, this is a cost-effective solution to service providers and customers. Secondly, for every job, virtual machine (VM) creations are avoided to reduce brokerage. The allocation, creation or selection of resources of VM is carried out by broker. The main objective is to maximize the resource utilization and minimize brokerage in cloud computing by using Multi-Objective Optimization (MOO). It considered a multi-attribute approach as it has more than two objectives. Likewise, CMRBC implements efficient resource allocation to reduce the usage cost of resources. The outcome of the experiment shows that CMRBC outperforms 60 percent of reduction in brokerage and 10 percent in response time.
How the governance of and through digital contact tracing technologies shapes geographies of power

In this article, we use the COVID-19 pandemic to study governance through digital technologies. We investigate ‘digital contact tracing’ (DCT) apps developed in Austria and Norway and find their emergence, contestation and stabilisation as moments in which norms and values are puzzled through, and distributions of power change. We show that debates on DCT apps involved disputes on ‘digital citizenship’, that is, on the scope and nature of data that authorities are allowed to collect from citizens. Remarkably, these disputes were settled through the enrolment of a framework developed jointly by Apple and Google. Software became akin to a constitution that enshrined understandings of good citizenship into technological design, while also being a means through which geographies of power materialised. This article contributes to literature on technological governance by showing how the rising salience of technologies in governance transform political geographies and, as a consequence, democratic lives.
Personalized Information Retrieval for Touristic Attractions in Augmented Reality

The rapid advances and increasing accessibility of augmented reality (AR) in recent years opened up many new possibilities to incorporate AR into our daily lives. A very interesting area for AR is tourism where one can enhance attractions with virtual elements and provide tourists with additional information about the places they are visiting. In this paper, we present our prototype, an AR application that augments various points of interest (POIs) by showing images and facts about each POI. We also developed a simple recommender system that ensures the facts are selected based on user preferences, thus creating a unique and personalized experience for each user. Furthermore, we also conducted a live user study to assess the usability of our prototype and the usefulness of our personalization system.
A Survey on Improved GAN based Image Inpainting

Image inpainting is a computer vision technique that originated from the restoration of broken artworks. It refers to the work of restoring or completing an image by using the information of the undamaged part of the image when there is a broken or empty local area in the image. Image inpainting technology has a wide range of applications in digital cultural heritage preservation, image editing, virtual reality and other fields. This paper introduces the traditional image inpainting methods briefly at first, and then focuses on the inpainting methods based on generative adversarial network models in deep learning, mainly covering the problems solved by the inpainting methods, the advantages and disadvantages of different inpainting methods, and as well the methods to be improved, etc. Finally, we analyze the difficulties in image inpainting, and point out the development in the future.
Mobile based Human Identification using Forehead Creases: Application and Assessment under COVID-19 Masked Face Scenarios

In the COVID-19 situation, face masks have become an essential part of our daily life. As mask occludes most prominent facial characteristics, it brings new challenges to the existing facial recognition systems. This paper presents an idea to consider forehead creases (under surprise facial expression) as a new biometric modality to authenticate mask-wearing faces. The forehead biometrics utilizes the creases and textural skin patterns appearing due to voluntary contraction of the forehead region as features. The proposed framework is an efficient and generalizable deep learning framework for forehead recognition. Face-selfie images are collected using smartphone’s frontal camera in an unconstrained environment with various indoor/outdoor realistic environments. Acquired forehead images are first subjected to a segmentation model that results in rectangular Region Of Interest (ROI’s). A set of convolutional feature maps are subsequently obtained using a backbone network. The primary embeddings are enriched using a dual attention network (DANet) to induce discriminative feature learning. The attention-empowered embeddings are then optimized using Large Margin Co-sine Loss (LMCL) followed by Focal Loss to update weights for inducting robust training and better feature discriminating capabilities. Our system is end-to-end and few-shot; thus, it is very efficient in memory requirements and recognition rate. Besides, we present a forehead image dataset (BITS-IITMandi-ForeheadCreases Images Database <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) that has been recorded in two sessions from 247 subjects containing a total of 4,964 selfie-face mask images. To the best of our knowledge, this is the first to date mobile-based fore-head dataset and is being made available along with the mobile application in the public domain. The proposed system has achieved high performance results in both closed-set, i.e., CRR of 99.08% and EER of 0.44% and open-set matching, i.e., CRR: 97.84%, EER: 12.40% which justifies the significance of using forehead as a biometric modality.
Semantic Segmentation of Tumors in Kidneys using Attention U-Net Models

Accurate segmentation of tumors in kidneys (renal) will assist clinical experts to identify the occurrence of cancer. Physical kidney segmentation on CT images is tedious and varies between individual professionals due to its diverseness. Therefore, deep convolutional neural networks are widely used in renal segmentation tasks to aid in the early detection of renal cancer. Using Attention U-Net architecture, we propose an automated technique for delineating the kidneys and tumor in computed tomography (CT) images. Attention U-Net models place a greater emphasis on regions of interest (kidneys and tumors) and less emphasis on areas that are not in focus. With 19 pre-exercised model segments, the Attention U-Net Architecture is utilized to segment kidney tumors (KiTS 2019). To improve the kidney and tumor IOU scores we ultimately ensemble six Top Models.
Multifractal features of activity concentration and stochastic risk assessment of naturally occurring and technogenic radionuclides in the soil of Yerevan, Armenia

Spatial patterns and background ranges of naturally occurring radionuclides (NORs) (i.e. U-238, Th-232, K-40) and Cs-137 were studied in the urban soils of Yerevan, the capital city of Armenia. Multifractal Inverse Distance Weighting (MIDW) was used to generate and analyze distribution patterns of radionuclide activities. Based on Fourier transformation of radioactivity data, a spectral analysis was also applied to separate, where possible, background/baseline patterns from local anomalies: two ranges of background values were found to characterise the Yerevan territory. Specifically, in the south and south-east of Yerevan, the lower background ranges of U-238, Th-232 and K-40 comprised in the intervals 2.60-36.42 Bq/kg, 4.04-30.63 Bq/kg and 147.7-396.7 Bq/kg, respectively, were observed in association with the presence of sedimentary formations. In contrast, the higher ones were found, instead, in the central and northern parts of the city where andesite-basalt lavas and ignimbrite tuffs occur. Here, the background values rise to 142.4 Bq/kg, 138.76 Bq/kg and 1502 Bq/kg, respectively. As for the distribution of artificial Cs-137, its baseline levels in Yerevan seem to depend mostly on the global radioactive fallout and some local technogenic sources. Its distribution patterns partially differ from those of NORs. In the framework of this paper, Radium equivalent activity (RaEq), outdoor absorbed dose rate in air (ODRA) and annual effective dose equivalent (AEDEs) were also determined and mapped. They show a good coincidence of their spatial variations with those of NORs. The Monte Carlo simulation was used to assess excess lifetime cancer risk from a stochastic perspective. The related sensitivity analysis revealed that, among NORs, U-238 and Th-232 give the greatest contribution to the total variance (45.7% 42.8%, respectively). In comparison, K-40 has the lowest share (11.3%). Regarding Cs-137, a highly negligible contribution to the onset of health risks (accounting for 0.02%) was observed.
Reasoning about epistemic social network dynamics using dynamic term-modal logic

Abstract Logics for social networks have been studied in the recent literature. This paper presents a framework based on dynamic term-modal logic ($\textsf {DTML}$), a quantified variant of dynamic epistemic logic (DEL). In contrast with DEL where it is commonly known to whom agent names refer, $\textsf {DTML}$ can represent dynamics with uncertainty about agent identity. We exemplify dynamics where such uncertainty and de re/de dicto distinctions are key to social network epistemics. Technically, we show that $\textsf {DTML}$ semantics can represent a popular class of hybrid logic epistemic social network models. We also show that $\textsf {DTML}$ can encode previously discussed dynamics for which finding a complete logic was left open. As complete reduction axioms systems exist for $\textsf {DTML}$, this yields a complete system for the dynamics in question.
The Presence of Background Noise Extends the Competitor Space in Native and Non‐Native Spoken‐Word Recognition: Insights from Computational Modeling

Oral communication often takes place in noisy environments, which challenge spoken-word recognition. Previous research has suggested that the presence of background noise extends the number of candidate words competing with the target word for recognition and that this extension affects the time course and accuracy of spoken-word recognition. In this study, we further investigated the temporal dynamics of competition processes in the presence of background noise, and how these vary in listeners with different language proficiency (i.e., native and non-native) using computational modeling. We developed ListenIN (Listen-In-Noise), a neural-network model based on an autoencoder architecture, which learns to map phonological forms onto meanings in two languages and simulates native and non-native spoken-word comprehension. We also examined the model's activation states during online spoken-word recognition. These analyses demonstrated that the presence of background noise increases the number of competitor words, which are engaged in phonological competition and that this happens in similar ways intra and interlinguistically and in native and non-native listening. Taken together, our results support accounts positing a "many-additional-competitors scenario" for the effects of noise on spoken-word recognition.
MABC‐EPF: Video in‐painting technique with enhanced priority function and optimal patch search algorithm

Abstract In this article, video in‐painting technique with enhanced priority function and optimal patch searching algorithm is proposed. Initially, the source frames are extracted from video based on edge information, these frames are partitioned into two regions (1) target region (region to be in‐painted) and (2) source region that is region to be considered as appropriate patch selection for in‐painting the target region. A new priority function using confidence value, local structure multiplier, and motion vector uniformity to determine the in‐paint location. The suitable patch is identified in source region, using correlation measure, like sum of squared difference method. The searching of the suitable patch in source region is optimized through modified artificial bee colony (MABC) algorithm. To capture more edge information, irregular patch filling is carried out to in‐painting process. The in‐painting process is more improved by optimal patch scaling for better vision. Finally, the in‐painted source frames are combined to obtain the corrected video. The proposed method works better than the other art state applications. The proposed peak signal to noise ratio (PSNR) attains better 8.06%, 7.90%, 32.15%, and 13.06% compared with other previous methods.
GradMFL: Gradient Memory-Based Federated Learning for Hierarchical Knowledge Transferring Over Non-IID Data

The massive datasets are often collected under non-IID distribution scenarios, which enforces existing federated learning (FL) frameworks to be still struggling on the model accuracy and convergence. To achieve heterogeneity-aware collaborative training, the FL server aggregates gradients from different clients to ingest and transfer common knowledge behind non-IID data, while leading to information loss and bias due to statistical weighting. To address the above issues, we propose a Gradient Memory-based Federated Learning (GradMFL) framework, which enables Hierarchical Knowledge Transferring over Non-IID Data. In GradMFL, a data clustering method is proposed to categorize Non-IID data to IID data according to the similarity. And then, in order to enable beneficial knowledge transferring between hierarchical clusters, we also present a multi-stage model training mechanism using gradient memory, constraining the updating directions. Experiments on solving a set of classification tasks based on benchmark datasets have shown the strong performance of good accuracy and high efficiency.
Construction of Knowledge Graph of Electrical Equipment Based on Sentence - BERT

The construction of the indicator system of power equipment is the basic work of the state assessment of power equipment, which is of great significance to maintain the stable operation of the equipment, Constructing the knowledge graph of the evaluation indicator of power equipment can provide data support for the state evaluation of power equipment. First, extract the indicators of each component of the equipment, establish the relationship between the indicators and the components, and form the hierarchical structure of the indicator system; Secondly, due to differences in the expression of indicators based on different standards, the Sentence-BERT (SBERT) network is used for knowledge fusion to achieve continuous improvement and update of knowledge in the knowledge graph; Finally, the constructed knowledge graph of the evaluation indicator of power equipment was visualized on Neo4j and its application was analyzed.