Reviewerly/exHarmony huggingface dataset:
MICaps: Multi-instance capsule network for machine inspection of Munro's microabscess

Munro's Microabscess (MM) is the diagnostic hallmark of psoriasis. Neutrophil detection in the Stratum Corneum (SC) of the skin epidermis is an integral part of MM detection in skin biopsy. The microscopic inspection of skin biopsy is a tedious task and staining variations in skin histopathology often hinder human performance to differentiate neutrophils from skin keratinocytes. Motivated from this, we propose a computational framework that can assist human experts and reduce potential errors in diagnosis. The framework first segments the SC layer, and multiple patches are sampled from the segmented regions which are classified to detect neutrophils. Both UNet and CapsNet are used for segmentation and classification. Experiments show that of the two choices, CapsNet, owing to its robustness towards better hierarchical object representation and localisation ability, appears as a better candidate for both segmentation and classification tasks and hence, we termed our framework as MICaps. The training algorithm explores both minimisation of Dice Loss and Focal Loss and makes a comparative study between the two. The proposed framework is validated with our in-house dataset consisting of 290 skin biopsy images. Two different experiments are considered. Under the first protocol, only 3-fold cross-validation is done to directly compare the current results with the state-of-the-art ones. Next, the performance of the system on a held-out data set is reported. The experimental results show that MICaps improves the state-of-the-art diagnosis performance by 3.27% (maximum) and reduces the number of model parameters by 50%.
Discovering obscure looking glass sites on the web to facilitate internet measurement research

Despite researchers have noticed that Looking Glass (LG) vantage points (VPs) are valuable for Internet measurement researches, they can only exploit VPs from well-known LG sites published on several LG portal pages. There should be a lot of LG sites that are not published in these portal pages, namely obscure LG sites, which are not easy to be found and exploited by researchers. In this paper, we design an efficient focused crawler to discover as many LG sites as possible which can avoid unnecessary resource consumption on analyzing irrelevant pages. Our designed focused crawler takes a similarity-guided search that exploits the well-developed search engines and comprehensively mines the common features shared by known LG sites to discover more LG pages. Moreover, the focused crawler takes a two-step PU learning classifier based on carefully selected LG features to efficiently discard irrelevant URLs, thus avoiding a lot of unnecessary resource consumption. As far as we know, we are the first to develop a method to discover obscure LG sites on the web. Experimental results show the effectiveness of our focused crawler. To facilitate practical applications, we further develop an automation tool, which can successfully retrieve 910 obscure automatable LG VPs from relevant pages obtained through our focused crawler. The 910 LG VPs significantly increase the geographic and network coverage of available VPs and we show their potential values in improving the completeness of AS-level Internet topology by a simple case study. Our method and the final VP list are beneficial to the measurement community.
Wideft: A Corpus Of Radio Frequency Signals For Wireless Device Fingerprint Research

Wireless network security may be improved by identifying networked devices via traits that are tied to hardware differences, typically related to unique variations introduced in the manufacturing process. One way these variations manifest is through unique transient events when a radio transmitter is activated or deactivated. Features extracted from these signal bursts have in some cases, shown to provide a unique “fingerprint” for a wireless device. However, only recently have researchers made such data available for research and comparison. Herein, we describe a publicly-available corpus of radio frequency signals that can be used for wireless device fingerprint research. The WIDEFT corpus contains signal bursts from 138 unique devices (100 bursts per device), including Bluetooth- and WiFi-enabled devices, from 79 unique models. Additionally, to demonstrate the utility of the WIDEFT corpus, we provide four baseline evaluations using a minimal subset of previously-proposed features and a simple ensemble classifier.
PrORAM

We construct a concretely efficient Zero Knowledge (ZK) Oblivious RAM (ORAM) for ZK Proof (ZKP) systems based on authenticated sharings of arithmetic values. It consumes \(2\log n\) oblivious transfers (OTs) of length-\(2\sigma \) secrets per access of an arithmetic value, for statistical security parameter \(\sigma \) and array size n. This is an asymptotic and concrete improvement over previous best (concretely efficient) ZK ORAM BubbleRAM of Heath and Kolesnikov ([], CCS 2020), whose access cost is \(\frac{1}{2}\log ^2 n\) OTs of length-\(2\sigma \) secrets.
Cartographic Objects in a Multiscale Data Structure

Displays should be more adaptable to users’ needs and their tasks instead of creating and presenting a static view of the data. In current approaches, objects to render are selected for each map and then transformed into cartographic objects to construct a map. This chapter considers the operation zooming as a new perspective on the problem of multiscale representations. The multiscale structure also supports multilevel modelling and combinations of objects in different scales in one map. Most geographic objects do not have directly visible and sharp boundaries (geological layers, soils); others do not have visible boundaries but boundary zones or undetermined boundaries (for example, forest, sand dunes). The problem to address is the selection of the objects in the forest which must be rendered. Two aspects can be separated, namely, the selection of objects which geometrically extend into a window and the selection of objects to achieve constant information density.
Learners’ Willingness to Communicate in a Foreign Language: The Role of Informal Digital Learning of English

The trend of online teaching and learning in the age of the COVID-19 pandemic has driven contemporary students to learn English in an informal context outside the classroom. Drawing on this issue, the present study is at the cutting edge of examining the role of Informal Digital Learning of English (IDLE) in affecting students’ willingness to communicate in a foreign language. A total of 156 (male=63; female=93) Indonesian EFL students participated in survey research upon invitation. The data are obtained by means of an online questionnaire consisting of three parts: (1) willingness to communicate, (2) IDLE, and (3) demographic data. The hierarchical regression analysis results reveal that Frequency of IDLE, Receptive IDLE activity, and Productive IDLE activity are the positive predictors of EFL learners’ willingness to communicate in English as a foreign language. The results indicate that the Indonesian EFL learners are motivated to communicate in English when engaging in a range of IDLE activities. In other words, it is implied that IDLE activities (both productive and receptive) potentially build the communication behavior of Indonesian EFL learners, who learn English as a foreign language and possess a culturally homogeneous society.
A Self-Sovereign Decentralized Identity Platform Based on Blockchain

Traditional accounts and passwords mechanism usually needs to register multiple accounts for adopting different scenarios, which makes it difficult to manage passwords and handle privacy leakage issues. On the other hand, users have no real control over their identities under centralized trusted authorities or insecure third-party operators. In this paper, we propose a self-sovereign decentralized identity platform called SSIChain. SSIChain uses distributed infrastructure blockchain to replace authorities and third-party operators. In the proposed platform, the DID and a credential represent an user's digital identity. We define DID chaincode and credential protocol to cover the full lifecycle of a user's digital identity. We build a complete consortium blockchain and application environment, and implement a prototype as an App for Android-based phones. Moreover, we detect that SSIChain only takes at most 2.1 seconds for authentication with high TPS, which can meet users' demands for identity management well.
Estimating Function Completion Time Distribution in Open Source FaaS

Function as a Service (FaaS) is the newest stage of application virtualization. Several public cloud providers offer FaaS solutions, however, the open source community also embraced this technology. In this paper we introduce a Python based function run-time, applicable in open source FaaS platforms for latency sensitive compute intensive applications that reduces the maximum completion times by taking into account the number of CPU cores. We also present our simulator that estimates the distribution of the completion times for compute intensive functions, when our proposed function run-time is in use. We present the results of our simulator by using two compute intensive functions. We also show a scenario when the user function is not purely compute intensive.
Hybrid Classifiers Based on CNN, LSOF, GMDH in COVID-19 Pneumonic Lesions Types Classification Task

The paper proposes new network tools for solving classification problems based on the hybridization of convolutional networks, self-organized forests, and Group Method of Data Handling classifiers. To determine the types of lung lesions in COVID-19 on computed tomography slices the new solutions have been applied. The features of the texture obtained from the matrices of GLCM, GLRLM, GLSZM, GLDM, NGTDM statistics are used here as classification features. The mechanism of transformation of texture matrices into class-oriented features based on hybrid architectures of neural networks is developed for this. The concept development of self-organized forest according to the principles of GMDH with the use of logistical mechanisms and optimization of voting functions (LSOF) is proposed. Classifiers based on CNN, LSOF, and GMDH are used to determine the type of lung lesions “ground-glass”, “crazy-paving”, “consolidation”on CT images of patients. The classification results obtained by the proposed algorithms have been compared with other modern methods. Data for the research were provided by the State Institution “F.G. Y anovsky National Institute of Phthisiology and Pulmonology of the NAMS of Ukraine”.
The Use of Transformer Model in Opinion Summarisation

We live in an age of information, therefore collected data and documentation are practically treasure resources. All about a business and its development can be estimated with clarity via statistics. Any machine that could really analyse information to predict a projected outcome is known for being extremely vital for the business. It is critical for the system to provide accurate and useful knowledge of the products in order to conduct accurate assessment. Summarisation is a technique for obtaining a rundown from series of sentences in a study or observation that facilitates us with understanding the basic content of the knowledge expressed within. Simple and brief summaries of just a product will assist the system in performing prospective product research and development. In our paper, we use a deep learning framework that provides to extract clean, relevant, brief summaries from comprehensive customer feedback. Strategies of abstractive text summarisation is used. The method of extracting the primary keyword from a statement and using them in the summary is defined as extractive text summarisation. We utilise abstractive summarisation in this case, which evolves from sample information and provides the best feasible description. Utilising Transformer with Depth Scaling MultiHeaded Attention as well as GloVe word embedding with positional encoding, we illustrate an abstractive approach to extract summaries from the Amazon fine food reviews dataset. Transformer aids in the parallelisation of workloads in order to process data more quickly. We have used an Attention layer which boost the model's quality and enables it to become more effective. The BLUE rating is used to quantify the model's potency.
Controlled-NOT operation of SiN-photonic circuit using photon pairs from silicon-photonic circuit

We demonstrate controlled-NOT (CNOT) operation of a silicon nitride photonic integrated circuit using path-entangled photon pairs from a silicon-photonic integrated circuit as logical inputs. The CNOT gate is constructed by combining several silicon-nitride Mach–Zehnder interferometers for a linear-optical quantum gate. We package each of the two photonic circuits with electrical wire bonding and optical fiber bonding for stable measurement. The logical-basis fidelity of CNOT operation is measured better than 81% for the fully packaged photonic circuits.
Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Kinship verification and recognition (KVR) is the machine's ability to identify the genetic and blood relationship and its degree between humans' facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems.We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation.It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images.Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.
Trajectory Optimization For Rendezvous Planning Using Quadratic Bézier Curves

In this paper, we consider a trajectory planning problem where an autonomous vehicle aims to rendezvous with another cooperating vehicle in minimum time. The first vehicle has kinematic constraints, consequently feasible trajectories must have a maximum curvature less than a specified limit. Rendezvous is said to occur at the instant that the two vehicles are collocated with the same heading. We propose a technique to construct a trajectory, composed of piecewise quadratic Bézier curves, that satisfies the vehicle motion constraints and achieves rendezvous in minimum time. The methodology begins by finding safe flight corridors, which are constructed from sequences of triangles using constrained Delaunay triangulation of the feasible space; the triangles define the bounds of Bézier curves. We formulate the necessary constraints for continuity and feasibility as functions of the control points that define the Bézier curves, and the resulting optimization problem is solved using a nonlinear programming solver. The techniques developed were tested using simulated scenarios, and we present the results which highlight the efficacy of the proposed solution approach. Furthermore, the algorithm was implemented and tested in a field test and those results are presented.
Self-efficacy in Information Security: A Replication Study

As an important construct in IS area, self-efficacy of individuals to engage in information security behaviors has been previously studied. We replicated one such study, which tested the influence of self-efficacy in information security (SEIS) on end user’s information security behavior. In our work, four out of six hypotheses were supported. By using different samples, our finding supported the original model and provided more research opportunities to apply this construct and model to different contexts and situations.
Unravelling social media racial discriminations through a semi-supervised approach

The study investigated cyber-racism on social media during the recent Coronavirus pandemic using a semi-supervised approach. Specifically, several machine learning models were trained to detect cyber-racism, followed by topic modelling using Latent Dirichlet Allocation (LDA). Twitter data were gathered using the hash tags Chinese virus and Kung Flu in the month of March 2020, resulting in 7,454 clean tweets. Negative tweets extracted using sentiment analysis were annotated (Racism, Sarcasm/irony and Others), and used to train several machine learning models. Experimental results show Random Forest with bagging to consistently outperform Random Forest, J48 and Support Vector Machine with an accuracy of 78.1% (Racism versus Sarcasm/Irony) and 77.9% (Racism versus Others). LDA revealed three distinct topics for tweets identified as racist, namely, Eating habit, Political hatred and Xenophobia. Consistent detection performance of the models evaluated indicate their reliability in detecting cyber-racism patterns based on textual communications.
Quantum secure direct communication based on quantum homomorphic encryption

As one of the most important branches of quantum cryptography, quantum secure direct communication (QSDC) is used to transmit the secret message directly rather than distribute a random key. Quantum homomorphic encryption (QHE) enables arbitrary quantum transformation on encrypted data without decrypting the data. To date, the previously proposed QSDC schemes are mainly based on different quantum states. The research of the QSDC scheme based on QHE is still blank. In this paper, a QSDC scheme by taking advantage of the properties of QHE is proposed. The proposed protocol has applied QHE and decoy photons to prevent various types of attacks. The proposed scheme only utilizes the rotation operation to encode the secret message which is easy to implement with the current technologies. Moreover, the communication efficiency and the qubit-utilization ratio are analyzed in this paper, which shows that this protocol has good performance in the qubit-utilization ratio, and the qubit efficiency of the QSDC scheme has improved.
Experimental interpretation of adequate weight-metric combination for dynamic user-based collaborative filtering

Recommender systems include a broad scope of applications and are associated with subjective preferences, indicating variations in recommendations. As a field of data science and machine learning, recommender systems require both statistical perspectives and sufficient performance monitoring. In this paper, we propose diversified similarity measurements by observing recommendation performance using generic metrics. Considering user-based collaborative filtering, the probability of an item being preferred by any user is measured. Having examined the best neighbor counts, we verified the test item bias phenomenon for similarity equations. Because of the statistical parameters used for computing in a global scope, there is implicit information in the literature, whether those parameters comprise the focal point user data statically. Regarding each dynamic prediction, user-wise parameters are expected to be generated at runtime by excluding the item of interest. This yields reliable results and is more compatible with real-time systems. Furthermore, we underline the effect of significance weighting by examining the similarities between a user of interest and its neighbors. Overall, this study uniquely combines significance weighting and test-item bias mitigation by inspecting the fine-tuned neighborhood. Consequently, the results reveal adequate similarity weight and performance metric combinations. The source code of our architecture is available at https://codeocean.com/capsule/1427708/tree/v1 .
Machine translation literacy: a panorama of practices at Swiss universities and implications for language teaching

This short paper presents the quantitative results of an online survey of Swiss university students and staff on their use of Machine Translation (MT). The analysis of the 3,713 responses throws light on the context, purposes, degree of successive revisions, and ethical considerations surrounding the use of MT. With regards to language teachers and students, the quantitative data allows us to draw three preliminary conclusions: MT is a well-established but unspoken practice in Swiss universities, MT is not seen as an alternative to language learning, and MT is seen and already being used as a tool to improve language skills.
Diffractive imaging and intermodulation based multiple image encryption

In this research, the HYBRID-III method has been developed to encrypt multiple images. The method, which is obtained by combining diffractive imaging and phase retrieval based intermodulation, can be used to encode multiple images as a single plane. The method contributes to the solution of the bandwidth problem of communication systems since it sends images as a single plane while encrypting them. Users can access only their own plaintext independently of others by using disposable mask codes. The method has been tested in different matrix sizes and different file structures. Moreover, the strength of the method against cyber-attacks is additionally tested.
SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland

SEM-based automated mineralogy (SEM-AM) techniques allow fast and effective way of studying the textural settings of gold in hydrothermal deposits. Unsupervised machine learning (e.g. self-organizing maps) is an intuitive way of processing multi-dimensional geochemical datasets in order to reveal hidden patterns potentially represent different mineralization stages. We combined these two methods for studying the relationship of gold and cobalt within different prospects in a Paleoproterozoic gold-cobalt mineralized area known as Rajapalot. Gold is found as a texturally late phase, occurring in fractures of silicates and sulfides. Based on the elemental associations observed from the whole-rock geochemical dataset using self-organizing-maps, Co-only, Au-Co and Au associations can be inferred relating to either different mineralization stages or different fluid-host rock interactions. Also, the dominant mineralization-related alteration in different occurrences within the Rajapalot Au-Co prospects are reflected as elemental associations with gold in the geochemical data. Our study shows the effectiveness SEM-AM methods for studying distribution of valuable minerals. Unsupervised neural networks provide for easy and intuitive processing technique that can be validated with the mineralogical observations.