Reviewerly/exHarmony huggingface dataset:
Impact of Good Corporate Practices for Security of Digital Products on Global Cyber Stability

The exploitation of vulnerabilities in digital products and services is an essential component of sophisticated cyberattacks. Well-resourced adversaries increasingly exploit vulnerabilities for economic, political, or military gain, causing effects that destabilise cyberspace. Several multilateral and multi-stakeholder fora develop norms and principles to reduce such vulnerabilities. The main challenge lies in implementation. Under the Geneva Dialogue on Responsible Behaviour in Cyberspace <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The Geneva Dialogue (https://genevadialogue.ch) is an initiative of the Swiss government and DiploFoundation. Partners of the Geneva Dialogue include Bi. Zone, Cisco, EnSign, FireEye Mandiant, Kaspersky, Huawei, Microsoft, UBS, PNG ICT Cluster, SICPA, Siemens, SwissRe, Tata Consultancy Services, VU, and Wisekey. Good corporate practices regarding the security of digital products and services, discussed in detail in this paper, have been developed through 15 group online meetings and continuous collaboration in the shared document, conducted over 7 months in 2020. 25 (Geneva Dialogue), a dozen leading global companies jointly developed a set of good corporate practices that translate high-level principles into day-to-day operations. This paper argues that these practices make cyberspace less vulnerable, and thus contribute to the implementation of global norms and principles. It further analyses key global norms and principles related to the security of digital products and services and the role of industry. It then presents the most relevant results of the ongoing work of the Geneva Dialogue, particularly good corporate practices related to security by design: threat modelling, supply chain security, development and deployment, and vulnerability processes. It discusses how these measures may reduce vulnerabilities, especially for smaller producers whose importance in the supply chain was elevated by COVID-19. It reflects on the need to turn good practices into baseline requirements to support market newcomers and regulators worldwide.
The Value of the User Evaluation Process in the European IoT Large-Scale Pilot for Smart Living

Within the EU ACTIVAGE [1] project, a multi-centric IoT-Large Scale Pilot on smart living environments for aging well, we enrolled our smart living solutions in Germany with 184 older adults and a number of their formal and informal caregivers as end users and performed in the last year of the project an evaluation of our deployments. This paper presents the results of this evaluation. The main goal of the assessment was to find an optimal set of devices and functions to increase the users' wellbeing based on IoT activity monitoring and detection of emergencies. The evaluation used information from sensors installed in homes and nursing rooms, combined with surveys on demographic data of users, user satisfaction survey, and a marketing survey to assess other features that the system should have to offer an attractive solution on the market. After an overview of the piloting setup and a focused discussion of the evaluation results, the paper concludes with further development, which resulted in a new system.
A population-based automatic clustering algorithm for image segmentation

Clustering is one of the prominent approaches for image segmentation. Conventional algorithms such as k-means, while extensively used for image segmentation, suffer from problems such as sensitivity to initialisation and getting stuck in local optima. To overcome these, population-based metaheuristic algorithms can be employed. This paper proposes a novel clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a powerful population-based algorithm to tackle optimisation problems. One of the advantages of our proposed algorithm is that it does not require any information about the number of clusters. To verify the effectiveness of our proposed algorithm, we present a set of experiments based on objective function evaluation and image segmentation criteria to show that our proposed algorithm outperforms existing approaches.
whole new world

This poster will present the results of a thematic analysis of the contents of the Vancouver Public Library’s (VPL) Instagram, Twitter, and YouTube feeds throughout the first wave of the COVID-19 pandemic (February 1st to June 30th, 2020) to better understand what types of crisis-related services and information they are providing to the public. This data will also be compared to Instagram, Twitter, and YouTube content from the same time period in 2019 to investigate whether the pandemic has changed VPL’s social media presence in any significant way.
EAWNet: An Edge Attention-Wise Objector for Real-Time Visual Internet of Things

With the upgrading of the high-performance image processing platform and visual internet of things sensors, VIOT is widely used in intelligent transportation, autopilot, military reconnaissance, public safety, and other fields. However, the outdoor visual internet of things system is very sensitive to the weather and unbalanced scale of latent object. The performance of supervised learning is often limited by the disturbance of abnormal data. It is difficult to collect all classes from limited historical instances. Therefore, in terms of the anomaly detection images, fast and accurate artificial intelligence-based object detection technology has become a research hot spot in the field of intelligent vision internet of things. To this end, we propose an efficient and accurate deep learning framework for real-time and dense object detection in VIOT named the Edge Attention-wise Convolutional Neural Network (EAWNet) with three main features. First, it can identify remote aerial and daily scenery objects fast and accurately in terms of an unbalanced category. Second, edge prior and rotated anchor are adopted to enhance the efficiency of detection in edge computing internet. Third, our EAWNet network uses an edge sensing object structure, makes full use of an attention mechanism to dynamically screen different kinds of objects, and performs target recognition on multiple scales. The edge recovery effect and target detection performance for long-distance aerial objects were significantly improved. We explore the efficiency of various architectures and fine tune the training process using various backbone and data enhancement strategies to increase the variety of the training data and overcome the size limitation of input images. Extensive experiments and comprehensive evaluation on COCO and large-scale DOTA datasets proved the effectiveness of this framework that achieved the most advanced performance in real-time VIOT object detection.
PrAVA: Preprocessing profiling approach for visual analytics

To accommodate the demands of a data-driven society, we have expanded our ability to collect and store data, develop sophisticated algorithms, and generate elaborated visual representations of the data analysis process outcomes. However, data preprocessing, as the activity of transforming the raw data into an appropriate format for subsequent analysis, is still a challenging part of this process. Although we can find studies that address the use of visualization techniques to support the activities in the scope of preprocessing, the current Visual Analytics processes do not consider preprocessing an equally important phase in their processes. Hence, with this paper, we aim to contribute to the discussion of how we can incorporate the preprocessing as a prominent phase in the Visual Analytics process and promote better alternatives to assist the data analysts during the preprocessing activities. To achieve that, we are introducing the Preprocessing Profiling Approach for Visual Analytics (PrAVA), a conceptual Visual Analytics process that includes Preprocessing Profiling as a new phase. It also contemplates a set of guidelines to be considered by new solutions adopting PrAVA. Moreover, we analyze its applicability through use case scenarios that show resourceful methods for data understanding and evaluation of the preprocessing impacts. As a final contribution, we indicate a list of research opportunities in the scope of preprocessing combined with visualization and Visual Analytics to stimulate a shift to visual preprocessing.
Power Passports for Fault Tolerance: Anomaly Detection in Industrial CPS Using Electrical EFB

We present how electrical Extra-Functional Behavioural (EFB) metrics can be used in generating accurate representations of executional units for industrial Cyber-Physical Systems (CPS). We achieve this by employing our concept of power passports (the representation), created per metric and per execution phase (executional unit). We employ these representations alongside supervised classification algorithms, i.e., Decision Tree and Random Forest, in an effective data analytical pipeline. Our approach is capable of detecting anomalous operational conditions and predicting the type of anomaly, out of different known types, with significant overall accuracies, as high as 99% in certain set-ups. We consider anomalous operational conditions as nonreference conditions, resulting in loss of performance or unreliable operation of a system. Our experiments are designed to reflect real-world conditions as much as possible and all of our collected raw data comes from real executions, normal and anomalous, with no synthetic manipulation. Our results show that a black box approach towards systems under scrutiny for anomaly detection and classification, given its accuracy and considering the limitations applicable to low-power industrial CPS, can be the preferred one.
A Hybrid Method of Multi-class SVM and Classification Method Based on Reliability Score for Autocoding of the Family Income and Expenditure Survey

The classification of text descriptions based on corresponding classes is an important task in official statistics. We developed a hybrid method of SVM utilizing Word2Vec and the previously developed reliability–score-based classifier to improve both the ability of high classification accuracy and generalization performance. However, in the previous study, as SVM was simply applied to a whole given data, there is room for more efficiently classifying those data to improve the classification accuracy. Therefore, this paper proposes a classification method based on multi-class SVM that is a combined method of SVM and k-means method to improve the ability of high classification accuracy. The numerical example shows the proposed method gives a better result as compared to the results of ordinary methods.
Quantum channels transforming Bell local states as unsteerable states

Motivated by the contributions [Sci. Rep. 6, 39063 (2016)] and [Phys. Rev. A 98, 052114 (2018)], we aim to establish a new method for theoretically checking Bell nonlocality of a bipartite state of systems for any finite dimensional Hilbert spaces and , provided that the operator space has an orthonormal basis consisting of the identity operator and Hermitian unitary operators. Our main result induces a quantum channel transforming a Bell local state as an unsteerable state from A to B whenever the parameters T and satisfy the condition . Thus, when and the image is steerable from Ato B, the original state must be Bell nonlocal. Our result not only reveals a deep connection between EPR steerability and Bell nonlocality but also provides a feasible approach to theoretically prove difficultly verified Bell nonlocality by translating it into easily verified steerability.
Performance analysis of facial recognition: A critical review through glass factor

COVID-19 pandemic and social distancing urge a reliable human face recognition system in different abnormal situations. However, there is no research which studies the influence of glass factor in facial recognition system. This paper provides a comprehensive review of glass factor. The study contains two steps: data collection and accuracy test. Data collection includes collecting human face images through different situations, such as clear glasses, glass with water and glass with mist. Based on the collected data, an existing state-of-the-art face detection and recognition system built upon MTCNN and Inception V1 deep nets is tested for further analysis. Experimental data supports that 1) the system is robust for classification when comparing real-time images and 2) it fails at determining if two images are of same person by comparing real-time disturbed image with the frontal ones.
Image Sharpness Control in Modern Television Systems

The article is devoted to establishing a connection between the subjective assessment of the TV images quality and the generalized criterion of definition in two dimensions. In modern practice in television definition is characterized by the number of active scan lines and the number of active picture elements per line. It should be taken into account that the achievable image definition is limited by diffraction distortions of the lens and blurring due to the finite dimensions of the "light-to-signal" and "signal-to-light" conversion elements of the matrix sensor camera and playback display screen. The question of what should be the MTF of an image processing system used to improve definition in two dimensions is relevant, based on the compromise between the attainable level of definition and the interfering effect of edging in the image arising as a result of excessive emphasis on image details. The article is devoted to answering this question, but the experimental data presented in it are focused on solving the more general problem of establishing the relationship between image quality and definition in two dimensions for the general case. In the experiments, to vary the resulting definition, a controlled two-dimensional corrector was used in the form of a digital filter with circular symmetry MTF. It was provided for the possibility of blurring the image by limiting the frequency band and correcting definition by using a corrector with a rise that varies according to the cosine-square law so that the maximum was achieved at a frequency equal to half the spatial Nyquist frequency, i.e. the number of scan lines per frame height. The relative volume under the two-dimensional MTF of the TV path "from light to light" was used as a generalized criterion for definition.
Elaboration of Digital Watermarking Method for a Protection of Cloning Attack on Paper Certificates

Paper (or plastic) certificates are considered as a means against different forgery of product quality and brand falsification. It is commonly to use barcodes or data matrices to solve this problem. However such approach does not work usually against such sophisticated attacks as cloning of certificate copies. In the current paper we propose to use a digital watermarking and estimation of inner noises of scanners and printers in order to detect cloning attack effectively. Algorithm of cloning attack detecting is presented. The probabilities of attack missing and false alarm are proved.
Voice Activation for Low-Resource Languages

Voice activation systems are used to find a pre-defined word or phrase in the audio stream. Industry solutions, such as “OK, Google” for Android devices, are trained with millions of samples. In this work, we propose and investigate several ways to train a voice activation system when the in-domain data set is small. We compare self-training exemplar pre-training, fine-tuning a model pre-trained on another domain, joint training on both an out-of-domain high-resource and a target low-resource data set, and unsupervised pre-training. In our experiments, the unsupervised pre-training and the joint-training with a high-resource data set from another domain significantly outperform a strong baseline of fine-tuning a model trained on another data set. We obtain 7–25% relative improvement depending on the model architecture. Additionally, we improve the best test accuracy on the Lithuanian data set from 90.77% to 93.85%.
NEXT! Toaster: Promoting Design Process with a Smart Assistant

This paper introduces the smart activity promotion assistant NEXT! Toaster prototype. In order to solve the "isolated dilemma" in design activities, NEXT! Toaster matches the appropriate design thinking tools to the activities, prints tools by phases, guides and trains designers to control the design process and achieve the design goals. This study also explores the dynamics of design activities with robotic members, the characteristics of participants in the process of human-machine co-creation, and suggestions for the development of the design robotic assistants.
Optimal solution approach on large scale data to avoid deadlocks in resource allocations

Resources and Processes are two elements of the computational approach that make large-scale data analytics applications for databases and database management systems possible. Processes are the actions that various components take in order to conduct analysis and deal with massive databases. Resources are necessary for process management because they enable computational methodologies and functions and offer crucial hard and soft frameworks that aid in improved process management. The purpose of this research is to provide a graph theoretic technique for allocating resources to processes in order to avoid deadlocks. The set of resources and processes are combined in this work to provide the set of vertices for a directed graph. The graph's edges have been collected as ordered pairs, with each ordered pair denoting a process’s request either for any resource or for a process's allocation of resources. Furthermore, centrality metrics have been utilized to find cycles in the graph and after the cycles have been acquired; those cycles with potential deadlocks have been found. The ranking of vertices based on centralities is used to identify cycles with the potential to generate deadlocks. After getting the deadlock cycles, those edges are identified using a matrix operation, which may be deleted or postponed for a fixed period in order to prevent deadlock or locate other allocations. The essential edges, or resource demands, necessary to ensure a smooth, deadlock-free processing are identified. The findings of this study are essential for resource managers to comprehend the meaning and use of reserved resources that can be utilized in small quantities to avoid resource allocation deadlocks.
The Juku Online Writing System as an Interactive College English Writing Instructional Tool in China

The purpose of this study is to investigate the effects of the Juku online writing system as an interactive writing tool.A process-genre approach was implemented with this tool to cultivate college students' English writing ability, one of the key competences in English currently promoted in China.Seventy-seven non-English major undergraduates at a comprehensive four-year university in Jilin Province were surveyed on their writing experiences in English class during a period of 12 weeks of writing practice.Descriptive statistics and t-tests by means of SPSS were used to analyze both surveys by students of their writing experiences as well as the effects of the Juku approach, and students' improvement in essay writing.The results showed that compared with a traditional product-based writing instruction approach, the Juku system's diversified evaluation, and interactive teaching instruction aroused students' enthusiasm for learning and self-confidence in writing and helped students develop good writing habits as well.Moreover, analysis of students' writing performance showed significant improvements in students' writing scores from the pre-test to the post-test.Students' improvements with respect to lexical variables, syntactic variables and discourse variables were also indicated.Based on these findings, educational implications for the promotion of writing instruction are presented at the end of the paper.
Language Processing and Python

Natural language processing is the communication between the humans and the computers. It is the field of computer science which incorporates artificial intelligence and linguistics where machine learning algorithms are used to analyze and process the enormous variety of data. This chapter delivers the fundamental concepts of language processing in Python such as text and word operations. It also gives the details about the preference of Python language for language processing and its advantages. It specifies the basic concept of variables, list, operators, looping statements in Python and explains how it can be implemented in language processing. It also specifies how a structured program can be written using Python, categorizing and tagging of words, how an information can be extracted from a text, syntactic and semantic analysis, and NLP applications. It also concentrates some of the research applications where NLP is applied and the challenges of NLP processing in the real-time area of applications.
Automatic Extraction of Emojiset

Emojisets are defined as sequences of emojis found within text. Our goal is to find and automatically extract the emojiset from a given text. To do this we introduce a new algorithm ExtractEmojiset which performs this task. With our algorithm, we then demonstrate a census of emojisets found within six tweet datasets. Overall, single emoji emojisets are the most common. We hope the usage of emojisets expands to the application in future analysis using sentiment analysis, machine learning, and deep learning.
Text Mining Using Twitter Data

The microblogging social networking service Twitter has been abuzz around the globe in the last decade. A number of allegations as well as exculpation of different types are being held against it. The list of pros and cons of social networks is huge. India on one hand had an abundance of internet access in last half of the decade. The growth of social media and its influence on people have affected the society in both good as well as in bad way. The following research was done in the month of September and October. The research was carried out on 13 lakh tweets approximately, collected over the course of a month from September to October providing insights about the different attributes of general tweets available on Twitter API for analysis. Insights include the hashtags, account mentions, sentiment, polarity, subject, and object of a tweet. The topics like Rhea Chakraborty and Sushant Singh Rajput, PM Narendra Modi's Birthday, IPL 2020 overshadowed the topics like COVID-19 and women's security.
Joint model of gradient magnitude and Gabor features via Spatio-Temporal slice

To form a high-performance video quality predictor, we developed a framework for full-reference (FR) video quality assessment that integrates Spatio-temporal slice analysis (STS) to create a high-performance predictor of video quality. However, both gradient and Gabor are spatial–temporal structural capturers used for the simultaneous extraction of both spatial and temporal features. In this paper, we proposed a novel VQA algorithm via a joint model of gradient magnitude and Gabor features (JMG) between the STS images of the reference videos and their distorted counterparts to assess the degradation of video quality effectively. Firstly, gradient magnitude and the Gabor filter were constructed to extract the spatiotemporal features of the video sequence. However, the two-feature model combined to predict the perceptual quality of frames. This new proposed VQA model is known as the horizontal and time STS (HT-JMG) model. To further investigate the influence of spatial dissimilarity, we combined the frame-by-frame spatial T-JMG(S) factor with the HT-JMG and propose another VQA model, called the time, horizontal, and vertical STS (THV-JMG) model. Finally, the results of the experiment showed that the proposed method has a strong correlation with subjective perception and is competitive with state-of-the-art full reference VQA models.