Reviewerly/exHarmony huggingface dataset:
Elemental inheritance evaluation for geochemical elements in soil of the Daliangshan area, China

The inheritance and migration characters of different elements in the soil profile can serve agriculture, environmental protection, mineral exploration and other related research fields. These characters are mainly determined by the various geochemical behaviours of different elements. However, the elemental behaviours for the same element might vary wildly due to various local physical, chemical and biological environments, because the transformation of rock to soil includes rock-weathering and pedogenic processes. Here, we propose a framework for evaluating the elemental inheritance and migration characters from the underlying rocks or C horizons during local rock-weathering and pedogenic processes. In this framework, random forest regression was used to evaluate the importance of controlling factors, i.e. parent material or rock, climate, vegetation and topography, for individual elements during both processes. Further hierarchical clustering analysis was used to group elements into three inheritance categories according to their controlling factors. The framework was then applied in the Daliangshan area. Finally, we give some suggestions on the assessment of mineral resources, environment and agriculture according to the elemental inheritance categories in the Daliangshan area. This methodological approach can be applied to other localalities. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis Supplementary material: The original data and average feature importance for 5000 simulations are available at https://doi.org/10.6084/m9.figshare.c.6274315
Typicality effect in data graphs

Some types of data graphs are more easily understood than others. Following the suggestion that typically encountered graphs may activate individuals’ cognitive schema quickly, this study investigated prior exposure to and typicality of three common graph types: vertical bar graphs, horizontal bar graphs and line graphs; and three common data patterns: rising, neutral and falling. Results from two samples ( N = 57 and N = 30) suggest that vertical bar graphs are encountered more frequently, are rated as more typical and are identified more quickly than horizontal bar graphs and line graphs; also that rising data patterns are more typical than falling and neutral data patterns. The findings contribute new knowledge about the hierarchical structure of graph schema and can inform design choices about which graph types might best facilitate viewers’ understanding of data visualizations.
Validation of forensic tools and methods: A primer for the digital forensics examiner

Abstract Real‐world laboratory use, controlled internal tests utilizing scientific principles, and peer review should be leveraged in a proper validation test plan of digital and multimedia forensic tools and methods. As the field of digital and multimedia forensics continues to grow and evolve as a science, the importance of proper scientific validation of tools and methods, in addition to the fundamental understanding of its purpose and required elements, will be more critical than ever. As such, this primer article discusses the basic scientific foundations of forensic tool and method validations, in addition to the legal baselines for validations and how they are interpreted in the court of law. Lastly, the recommended steps of a forensically‐sound validation methodology are discussed. This article is categorized under: Digital and Multimedia Science &gt; Mobile Forensics Digital and Multimedia Science &gt; Multimedia Forensics Digital and Multimedia Science &gt; Cybercrime Investigation
What does it mean to trust blockchain technology?

Abstract This paper argues that the widespread belief that interactions between blockchains and their users are trust‐free is inaccurate and misleading, since this belief not only overlooks the vital role played by trust in the lack of knowledge and control but also conceals the moral and normative relevance of relying on blockchain applications. The paper reaches this argument by providing a close philosophical examination of the concept referred to as trust in blockchain technology, clarifying the trustor group, the structure, and the normatively loaded nature of this trust relation. The paper ends by critically reflecting on two of the most promising values (decentralization and transparency) that can invite users’ trust in blockchain technology, arguing that there is a tension between the pressing values that are intended to be achieved by developers and the predicament situations caused by current blockchain implementations.
The truth below the surface

The recipient is a stimulus-external factor that has so far hardly been investigated in hate-speech research. However, addressing this factor is essential to understand how and why hate speech unfolds its negative effects and which characteristics of the recipient influence these effects. The present study focuses on the recipient. Building on previous findings from explicit ratings and initial successful replications of such ratings through biosignals, we are conducting the first large-scale, systematic, and cross-linguistic biosignal study on hate speech based on two EEG measures: the beta-alpha ratio associated with arousal and the frontal alpha asymmetry associated with valence. A total of 50 Danish and German participants took part and were presented with spoken and written hate-speech stimuli, derived from authentic hate-speech posts on Twitter. Results show that Danes reacted more sensitively than Germans to hate speech containing figurative language (swear words), while Germans reacted more sensitively to hate speech with Holocaust references than Danes. In addition, teachers and lawyers showed less negative reactions to hate speech than church employees, students, and pensioners. The effect of the presentation medium depended on the respective hate speech type. In particular, speaking out hate speech based on irony and indirectness attenuated its effects on recipients to such an extent that it is questionable whether the stimuli were still perceived as instances of hate speech at all. We discuss the results in terms of key tasks of future studies and practical implication for the punishment and management of hate speech on social media.
Perceived usefulness of COVID-19 tools for contact tracing among contact tracers in Korea

In Korea, contact tracing for coronavirus disease 2019 is conducted using information from credit card records, handwritten visitor logs, KI-Pass (QR code), and the Safe Call system after an interview. We investigated the usefulness of these tools for contact tracing.An anonymous survey was conducted for 2 months (July to September 2021) among contact tracers throughout Korea. The questionnaire consisted of 4 parts: (1) demographic characteristics; (2) the usefulness of each tool for contact tracing; (3) the order in which information was checked during contact tracing; and (4) the match rate between tools for contact tracing, screening test rate, response rate, and helpfulness (rated on a Likert scale).In total, 190 individuals completed the survey. When asked to rate the usefulness of each tool for contact tracing on a Likert scale, most respondents (86.3%) provided positive responses for credit card records, while the most common responses for handwritten visitor logs were negative. The highest percentage of positive responses for helpfulness was found for KI-Pass (91.1%), followed in descending order by credit card records (82.6%), Safe Call (78.2%), and handwritten visitor logs (22.1%).Over 80% of participants provided positive responses for credit card records, KI-Pass, and Safe Call data, while approximately 50% provided negative responses regarding the usefulness of handwritten visitor logs. Our findings highlight the need to unify systems for post-interview contact tracing to increase their convenience for contact tracers, as well as the need to improve tools utilizing handwritten visitor logs for digitally vulnerable groups.
Affix substitution in Indonesian: A computational modeling approach

Abstract Indonesian has two noun-forming prefixes, PE- and PEN- , that often stand in a paradigmatic relation to verbal base words with the prefixes BER- and MEN- . The central question addressed in the present study is whether the form similarities between PEN- and MEN- make PEN- easier to learn compared to PE- . To address this question, we made use of a computational model, the ‘discriminative lexicon’ (DL) model. We trained this model on 2,517 word forms that were inflected or derived variants of 99 different base words. Of these word forms, 109 were nouns with PE- and 221 words were nouns with PEN- . Both the production and the comprehension networks of the model performed with high accuracy for both prefixes. However, the model was able to provide more precise predictions for PE- as compared to PEN- , implying that PE- should have a processing advantage compared to PEN- . There are two reasons for why PE- is learned more robustly than PEN- . First, PE- words tend to be longer and hence have more discriminative triphones. Second, due to cue competition with MEN- , the prefixal triphones of PEN- are less effective cues than those of PE- . A measure of functional load is proposed that helps clarify the relative importance of the triphones in the prefixes and those straddling the boundary between prefix and stem. Our results shed further light on the productivity paradox, role of junctural phonotactics, and (dis)functionality of affix substitution.
Recognition Models for Distribution and Out-of-Distribution of Human Activities

By monitoring movements and activities, the pro-gression of neurological diseases can be detected. The implemen-tation of such monitoring requires a high level of documentation, which is hardly possible in view of the ever-increasing shortage of nursing staff. In cooperation with two dementia residential communities, we are trying to gradually relieve the burden on nursing staff by developing an approach to automated documentation. In the attempt to recognise activities in the dementia environment, everyday activities can be well recognised using smartwatch sensor technology and machine learning, as shown in previous results from this research group. However, the literature lags behind (can hardly be found in the literature) on how to distinguish an activity from a non-activity, as a person does not perform an activity to be classified at all times. This paper explores a model to solve this problem, taking several approaches: Approach 1: First step classification to distinguish activity < - > non-activity. Second step activity detection using LSTM if activity was detected in step 1. Approach 2: First step differentiation of activity < - > non-activity directly with LSTM. Second step activity detection with LSTM if activity was detected in step 1. Approach 3: Direct distinction of activity < - > non-activity and activity detection with an LSTM. We show the advantages of the respective smartwatch sensor technology, compare the different approaches of our models to the prediction accuracy of the classification of different activities.
HTTP Adaptive Streaming System Maximizing Overall Video Quality over SDN-enabled Wi-Fi APs

This work presents a novel HTTP adaptive streaming system to maximize overall video streaming service quality of all the clients over SDN-enabled Wi-Fi APs. To archive the goal, the proposed system employs multipath technology to overcome the limitation of a Wi-Fi AP, and SDN technology to effectively harmonize multiple Wi-Fi APs. During the streaming services, SDN controller periodically monitors the traffic information collected from the APs, and then determines video bitrate and segment transmission schedule based on this information. The proposed system is fully implemented in real Wi-Fi network environments.
Training and Validating of Advanced Flow-Based Network Traffic Classifiers under Real-World Conditions

Robust network traffic classification (NTC) is an essential component of intrusion detection and intrusion prevention systems as well as quality of service applications. Modern NTCs use flow-based methods to either support or replace single-packet-based methods, since network traffic is usually encrypted. The flow-based methods are often implemented using neural networks. These neural networks are usually trained and tested using the same dataset. We propose a new testing method in which the data used to test the neural network occurs at a later time than the data used to train the neural network. For this, we used a dataset recorded by a honeypod that captured attacks, particularly on the SSH service, as well as authorized logins. The data was recorded over a period of over two years. Past data was used for training and recent data was used for testing, with a large time gap between the last datapoint of the training data and the first datapoint of the testing data. We show that existing neural network models developed for NTC are able to classify the attacks in our test scenario equally well, for authorized accesses the quality is worse. This shows that the proposed learning method for our chosen models is sufficient to robustly classify future network traffic in the scenarios tested here.
Differentiable Hierarchical Optimal Transport for Robust Multi-View Learning

Traditional multi-view learning methods often rely on two assumptions: ( i) the samples in different views are well-aligned, and ( ii) their representations obey the same distribution in a latent space. Unfortunately, these two assumptions may be questionable in practice, which limits the application of multi-view learning. In this work, we propose a differentiable hierarchical optimal transport (DHOT) method to mitigate the dependency of multi-view learning on these two assumptions. Given arbitrary two views of unaligned multi-view data, the DHOT method calculates the sliced Wasserstein distance between their latent distributions. Based on these sliced Wasserstein distances, the DHOT method further calculates the entropic optimal transport across different views and explicitly indicates the clustering structure of the views. Accordingly, the entropic optimal transport, together with the underlying sliced Wasserstein distances, leads to a hierarchical optimal transport distance defined for unaligned multi-view data, which works as the objective function of multi-view learning and leads to a bi-level optimization task. Moreover, our DHOT method treats the entropic optimal transport as a differentiable operator of model parameters. It considers the gradient of the entropic optimal transport in the backpropagation step and thus helps improve the descent direction for the model in the training phase. We demonstrate the superiority of our bi-level optimization strategy by comparing it to the traditional alternating optimization strategy. The DHOT method is applicable for both unsupervised and semi-supervised learning. Experimental results show that our DHOT method is at least comparable to state-of-the-art multi-view learning methods on both synthetic and real-world tasks, especially for challenging scenarios with unaligned multi-view data.
Secure word-level sorting based on fully homomorphic encryption

Fully homomorphic encryption enables to perform arbitrary computations over encrypted data opening a new avenue for the secure outsourced computation in cloud computing. Data sorting is a classical computational problem in computer science. Previously, all the secure sorting algorithms based on fully homomorphic encryption made use of the bit-level encryption, which requires that each bit of the input data is encrypted separately. However, the bit-level encryption incurs expensive computational overheads and a large ciphertext expansion. In this paper, we propose a secure word-level sorting scheme based on fully homomorphic encryption. We introduce a set of novel techniques to design an efficient word-level sorting algorithm. These include a new technique to compare two encrypted integers, a constant extraction technique to postprocess the output ciphertext of the comparison algorithm, an equality test method to check whether two ciphertexts encrypt the same integer and the single instruction multiple data technique to reduce the ciphertext sizes and improve the computational performance. The experimental results show that our word-level sorting algorithm outperforms the state-of-the-art bit-level sorting algorithm by more than 10 times.
TIM: Secure and usable authentication for smartphones

With the widespread popularity of smartphones and mobile applications, they have gradually penetrated and are widely used in our daily lives, e.g., we use them for online shopping and mobile banking. This has led to an increased demand for securing data processed and stored by smartphones. User authentication is an entry guard for ensuring secure access to smartphones, which aims at verifying a user’s identity. Typically, such a method is text-based authentication. However, the existing text-based authentication solutions bring in the trade-off issue between security and usability. The main reason is short text-based passwords are easy to remember but not secure enough as they are vulnerable to password guessing or shoulder surfing attacks. In contrast, long text-based passwords can ensure security, but they raise usability issues due to the difficulty of memorising, recalling, and inputting passwords. Moreover, the graphical password solutions suffer from shoulder surfing attacks. In this article, we propose an image-based authentication solution for smartphone users to reduce the risk of mounting a shoulder surfing attack. The proposed solution requires users to select and move predefined images to the designated position for passing the authentication check. In a laboratory experiment with 62 participants, we asked them to test the robustness of TIM to resist the existing attacks, and compare the usability with other image-based solutions. An analysis of collected results indicates that the proposed solution can resist password guessing and shoulder surfing attacks. Consequently, more than 85% of participants believed that the proposed solution could mitigate both password guessing and shoulder surfing attacks. Further, 71% of participants think that TIM is more usable compared to the existing solutions. While 50% of them preferred to choose TIM, more than 50% of participants claim that the learning curve of TIM is very short and the configuration is easy.
Cybercrime perspectives to the ‘ENDSARS’ protest in Nigeria

ABSTRACTABSTRACTCybercrime investigators, in an attempt to rid Nigeria of cybercrime, go about arresting and demanding for digital devices, of unsuspecting members of the public, especially Nigerian youths. Upon seizing these digital devices, they compel the owners to surrender security features to gain access to the devices to fish for cybercrime evidence. The rampant nature of this investigative process partly culminated in the recent 'ENDSARS' protests in Nigeria that commenced in cyberspace with the hashtag #ENDSARS NOW. Although the ENDSARS protest has become a topic of discourse in the news, literature, and academic domain, there seems to be limited or no literature on the cybercrime perspectives of the protests. This paper seeks to answer the question: To what extent can cybercrime investigators legally gain access to digital devices and social networks of persons? It concludes that to avoid future protests against cybercrime investigators, implementing a structured cybercrime investigation process in conformity with best practices, as it is done in developed economies like the United States of America, is suggested.KEYWORDS: Cybercrimecybercrime lawENDSARScyberspacedigital devicesNigeria Additional informationNotes on contributorsNewman U. RichardsDr. Newman U. Richards, holds PhD, LLM and LLB degrees from the Nnamdi Azikiwe University, Awka, Nigeria and the University of Benin, Benin City, Nigeria, respectively. He is a Senior Lecturer at the Faculty of Law, University of Nigeria, Enugu Campus, Enugu, Nigeria where he teaches Tax Law.Felix E. EboibiDr. Felix E. Eboibi holds PhD, LL.M & LL.B degrees from the University of Nigeria, Nsukka, Nigeria and the University of Calabar, Calabar, Nigeria, respectively. He is an Associate Professor of Cybercrime Law, Faculty of Law, Niger Delta University, Wilberforce Island, Bayelsa State, Nigeria, where he teaches Cybercrime Law and Computer Law.
Can relearning local representation help small networks for human pose estimation?

Human pose estimation is a special detection task for small object localization. It requires considering not only global structure but local and fine detail due to variable body poses and complex scenes. However, with the sliding window learning mechanism, the convolutional neural network (CNN) can only see the spatial information in a specific size of receptive field in a certain layer. As the network deepens and the receptive field becomes larger, the network gradually focuses on the global spatial information and loses the perception of local features. To help the deep convolutional neural network have the ability to relearn local information for structure analysis in deeper layers, we propose a layer-channel mixed attention mechanism named integrated attention that can be flexibly embedded into a CNN. Multiple features from the previous layers are aggregated to build attention with synchronously observing different ranges of spatial structures. Through our integrated attention, the network can observe the interdependence between local structures across different receptive fields and more clues can be learned to enhance the expressive power of the network for feature learning. The results of extensive experiments show that the integrated attention mechanism is beneficial to human pose estimation. In particular, the integrated attention can help small networks achieve more accurate predictions and even outperforms larger ones with less computation and parameters. Compared with other attention and keypoint refinement modules, our improvement effect is more stable and better.
EBDL: Effective blockchain-based covert storage channel with dynamic labels

Blockchain-based covert communication has opened up a new research direction for the covert communication field. In blockchain-based storage covert communication, reliability can be guaranteed because of the inherent immutable property of blockchain. Besides, enhanced undetectability is also achieved because the sender and the receiver do not need to establish a direct connection or be online simultaneously. However, in existing blockchain-based solutions, pre-negotiated and fixed addresses are used as static labels to scan the specific transaction that embeds covert messages, which increases the risk of channel exposure. Moreover, some of them do not support large covert message transmission. In this paper, we present an effective blockchain-based covert storage channel with dynamic labels. More specifically, a novel dynamic label mechanism is proposed to help the receiver extract the covert message efficiently, whereas adversaries cannot distinguish whether a block contains specific transactions. Besides, a message segmentation mechanism is also proposed to achieve large covert message transmission. Theoretical security analysis and detailed experimental evaluations based on the public Ethereum blockchain network show that the proposed scheme is secure under Chosen Hidden-text Attacks with acceptable efficiency.