Reviewerly/exHarmony huggingface dataset:
Compositional variation in garnets from different rocks of eastern ghat mobile belt around digapahandi area, Ganjam District, Odisha

As the relation between garnet chemistry and metamorphic condition is traditional, the importance of garnet in metamorphic petrology needs to be better understood. Garnet group of minerals provide information in terms of chemical composition, by utilizing which one can estimate the metamorphic P-T conditions, under which a rock has been formed. Khondalites, leptynites, charnockites and patchy charnockites are the important rocks exposed around Digapahandi area of Eastern Ghat Mobile Belt (EGMB) and garnet is a common mineral phase present in all these rock type. Hence garnets from each rock type were selected (total 39 points) for electron probe micro analysis (EPMA). A distinct compositional variation exists amongst garnets from different rocks, with abundant almandine content (67.845–88.028) and subordinate amount of pyrope, spessartine and grossular. These compositional variations in garnets from the different rocks of EGMB are not only due to variation in bulk rock composition but also due to distinct temperature and pressure conditions of metamorphism of these rocks. There is also a discrete variation in garnet composition from core to rim in each rock type. Growth zoning have developed in garnets from charnockites and khondalites while diffusion zoning is observed in the garnets from leptynites and patchy charnockites. Garnets in charnockites and khondalites are believed to have formed by prograde metamorphism during peak metamorphism. However garnets in leptynites and patchy charnockites are opined to have originated by retrograde metamorphism during exhumation following peak metamorphism.
AN AUTHENTICATION FOR GROUP COMMUNICATIONS IN AD-HOC NETWORKS

Group communication implies a many-to-many communication and it goes beyond both one-to-one communication (i.e., unicast) and one-to-many communication (i.e., multicast). Unlike most user authentication protocols that authenticate a single user each time, we propose a new type of authentication, called group authentication that authenticates all users in a group at once. The group authentication protocol is specially designed to support group communications. There is a group manager who is responsible to manage the group communication. During registration, each user of a group obtains an unique token from the group manager. Users present their tokens to determine whether they all belong to the same group or not. The group authentication protocol allows users to reuse their tokens without compromising the security of tokens. In addition, the group authentication can protect the identity of each user.
A Monocular Forward Leading Vehicle Distance Estimation using Mobile Devices.

Keeping the safe distance from the leading vehicle is crucial for transportation companies with a fleet of old cars. While modern Advanced Driver Assistant Systems (ADAS) might be able to estimate the distance from the front-leading vehicle, traditional ADAS do not usually offer this feature. An alternative solution is to monitor the distance using smartphones that are attached to a place such as a sun visor. The basic idea behind this approach is to detect the front-leading vehicle using the smartphone camera and estimate its distance from the car. Although SSD can achieve real-time performance on powerful GPUs, it remains challenging to run this model in real-time on mobile devices. In this paper, we propose a monocular distance estimator for forward-leading vehicles using a smartphone which is faster and more accurate than the state-of-the-art SSD detector. Specifically, we propose a layer-wise method to generate more efficient default boxes for the SSD and develop a lightweight method for estimating the distance accurately. Our experiments show that the proposed method reduces the number of default boxes by an average of 38.4% while it improves the detection rate and the processing speed compared to the original SSD. Moreover, our monocular distance estimator provides a proper safety buffer zone when the distance is greater than 20 meters. A sample video is available at https://youtu.be/-ptvfabBZWA.
The Myth of the Cyber Offense: The Case for Cyber Restraint

In the context of recent shifts in cybersecurity policy in the United States, this paper examines the character of cyber conflict through time. Data on cyber actions from 2000 to 2016 demonstrate evidence of a restrained domain with few aggressive attacks that seek a dramatic, decisive impact. Attacks do not beget attacks, nor do they deter them. But if few operations are effective in compelling the enemy and fewer still lead to responses in the domain, why would a policy of offensive operations to deter rival states be useful in cyberspace?We demonstrate that, while cyber operations to date have not been escalatory or particularly effective in achieving decisive outcomes, recent policy changes and strategy pronouncements by the Trump administration increase the risk of escalation while doing nothing to make cyber operations more effective. These changes revolve around a dangerous myth: offense is an effective and easy way to stop rival states from hacking America. New policies for authorizing preemptive offensive cyber strategies risk crossing a threshold and changing the rules of the game.
Towards Trusted Data Processing for Information and Intelligence Systems

Data is a valued asset and its security is essential for any enterprise and organization.This paper introduces Trusted Data Processing (TDP) and addresses three fundamental questions in TDP: 1) what are the essential requirements to achieve TDP? 2) what security mechanisms and safeguards are available to ensure TDP? 3) how to integrate TDP to practice?Based on the attacks targeting at data assets and their consequences, the requirements to achieve TDP, including data security, data privacy, accountability, transparency, distributed computing, and trusted elements, are identified.Available security mechanisms and safeguards to ensure TDP are discussed.This paper also summarizes the challenges to achieve TDP and provides a practical guidance to achieve TDP through the integration with NIST Cybersecurity Framework.
Glyfn: A Glyph-Aware Fusion Network for Distributed Chinese Event Detection

Recently, Chinese event detection has attracted more and more attention. As a special kind of hieroglyphics, Chinese glyphs are semantically useful but still unexplored in this task. In this paper, we propose a novel Glyph-Aware Fusion Network, named GlyFN. It introduces the glyphs' information into the pre-trained language model representation. To obtain a better representation, we design a Vector Linear Fusion mechanism to fuse them. Specifically, it first utilizes a max-pooling to capture salient information. Then, we use the linear operation of vectors to retain unique information. Moreover, for large-scale unstructured text, we distribute the data into different clusters parallelly. Finally, we conduct extensive experiments on ACE2005 and large-scale data. Experimental results show that GlyFN obtains increases of 7.48(10.18%) and 6.17(8.7%) in the F1-score for trigger identification and classification over the state-of-the-art methods, respectively. Furthermore, the event detection task for large-scale unstructured text can be efficiently accomplished through distribution.
DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT

Despite increasing instances of machine translation (MT) systems including extrasentential context information, the evidence for translation quality improvement is sparse, especially for discourse phenomena. Popular metrics like BLEU are not expressive or sensitive enough to capture quality improvements or drops that are minor in size but significant in perception. We introduce the first of their kind MT benchmark testsets that aim to track and hail improvements across four main discourse phenomena: anaphora, lexical consistency, coherence and readability, and discourse connective translation. We also introduce evaluation methods for these tasks, and evaluate several competitive baseline MT systems on the curated datasets. Surprisingly, we find that the complex context-aware models that we test do not improve discourse-related translations consistently across languages and phenomena. Our evaluation benchmark is available as a leaderboard at .
Constructionists’ approaches to information literacy: exploring Savolainen’s everyday life information seeking in information literacy research

This paper explores current issues in information literacy specifically as it relates to Savolainen’s literary work on Everyday Life Information Seeking. It aims to examine how information literacy research has evolved and how Savolainen’s work derived from a social constructionists’ approach contribute to current information literacy thinking and practice. The methodology used is conceptual analysis. Based on the outcome of the literature review and analysis of the research questions of the paper, the importance of the concepts of information literacy and Everyday Life Information Seeking are emphasised in ways that contribute to previous research and aspects relating to under-studied areas of information literacy research.
Routing Algorithm Experimental Teaching Design of Wireless Sensor Network for Internet of Things Engineering Specialty

Wireless sensor network is a core course of Internet of things engineering and routing algorithm experiment is a key and difficult part of this course as it has a high demand for related theoretical knowledge. In practical teaching, students are typically exposed to ambiguous understanding of algorithm and poor simulation results in the analysis of relevant experimental results. For this reason, experimental cases of density routing algorithm design in the experimental course are involved and advantages and disadvantages of different algorithms are thoroughly investigated by optimizing algorithm design and improving the simulation experiment verification link. In this way, the students’ capability of algorithm optimization and verification can be enhanced.
Meta Gradient Boosting Neural Networks

Meta-optimization is an effective approach that learns a shared set of parameters across tasks for parameter initialization in meta-learning. A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning. To address this issue, we design a meta-gradient boosting framework that uses a base learner to learn shared information across tasks and a series of gradient-boosted modules to capture task-specific information to fit diverse distributions. We evaluate the proposed model on both regression and classification tasks with multi-mode distributions. The results demonstrate both the effectiveness of our model in modulating task-specific meta-learned priors and its advantages on multi-mode distributions.
Teacher who filed for unemployment benefits may be one of the million-plus at risk of data breach

State and federal law enforcement agencies are investigating a sophisticated cyberattack targeting a California-based software company that provides services to the Washington State Auditor’s Office (SAO).
Discerning Feature Supported Encoder for Image Representation

Inspired by the recent successes of deep architecture, the auto-encoder and its variants have been intensively explored on image clustering and classification tasks by learning effective feature representations. Conventional auto-encoder attempts to uncover the data’s intrinsic structure, by constraining the output to be as much identical to the input as possible, which denotes that the hidden representation could faithfully reconstruct the input data. One issue that arises, however, is that such representations might not be optimized for specific tasks, e.g., image classification and clustering, since it compresses not only the discriminative information but also a lot of redundant or even noise within data. In other words, not all hidden units would benefit the specific tasks, while partial units are mainly used to represent the task-irrelevant patterns. In this paper, a general framework named discerning feature supported encoder (DFSE) is proposed, which integrates the auto-encoder and feature selection together into a unified model. Specifically, the feature selection is adapted to learned hidden-layer features to capture the task-relevant ones from the task-irrelevant ones. Meanwhile, the selected hidden units could in turn encode more discriminability only on the selected task-relevant units. To this end, our proposed algorithm can generate more effective image representation by distinguishing the task-relevant features from the task-irrelevant ones. Two scenarios of the experiments on image classification and clustering are conducted to evaluate our algorithm. The experiments on several benchmarks demonstrate that our method can achieve better performance over the state-of-the-art approaches in two scenarios.
Reading Motivations and Its Relationship with The Readability of Arabic Reference Books at The Malaysian Institute of Teacher Education

Reading motivation is usually associated with an understanding of the reading material being read. Readability, on the other hand, involves the level of difficulty or ease of a reading material according to the suitability of the reader. As Arabic is included in the category of foreign languages ​​in the context of language learning in Malaysia, issues related to the readability of Arabic reference books are often highlighted. This research intended to identify the level of reading motivation among trainee teachers of the Institute of Teacher Education (IPG) and its relationship with the readability of Arabic reference books. This quantitative study in the form of a survey adopted cloze tests and questionnaires adapted from Reading Motivation for Foreign Language Scale-Setsuko Mori to determine the level of reading motivation of trainee teachers. The study sample was a sum of 302 trainee teachers in the field of Arabic Language and Islamic Education who are studying at the Institute of Campus Teacher Education nationwide. Descriptive and inferential analysis was used to analyse the study data. The conclusions of the study confirmed that the level of reading motivation of trainee teachers was at a moderate level. Furthermore, the level of readability of trainee teachers was disappointing. The findings also recorded that there was a moderate positive relationship between the level of reading motivation and the readability of Arabic reference books (r = 0.324; p <0.05). Based on the outcomes of the study, it could be concluded that there was a moderate positive relationship between reading motivation and readability of Arabic books. This study implied that several strategies must be administered to boost the motivation to read Arabic among trainee teachers to increase the level of readability of Arabic books.
“È importante essere bravo”: Riflessioni antropologiche su dinamiche linguistiche e accoglienza

This article proposes an ethnographic and anthropological reflection conducted in the context of a sociolinguistic investigation of the linguistic dynamics between locals and asylum seekers in the Aosta Valley. The analysis of language practices involving migrants and local actors offers a series of elements that prove useful, in addition to those deriving from ethnographic observation, in clarifying the relationship between practices and rhetorics of hospitality. Investigating the linguistic dynamics, in fact, allowed to bring out opaqueness, ambiguity and contradictory and ambivalent rhetoric in the narratives of those involved, with varied positions and roles, in the hospitality process. On the one hand, we will highlight a paternalistic language based on an asymmetrical logic of expectations that structures the relationship between different figures, even in a positive context of widespread hospitality. Furthermore, we will focus on how the hospitality system, that structurally relies on voluntary activities, acts through rhetoric and practices of discharging of responsibilities on the individual level, being it represented by a migrant who must demonstrate autonomy, or a volunteer, to whom it is often delegated the difficult task of accompanying the path of integration.
L'alfabetizzazione nella classe plurilingue. Un'indagine a Bologna

The present paper analyses multilingual practices in the primary schools of Bologna (Italy). More specifically, we investigate the linguistic repertoires of a sample of 78 students, as well as their self-assessments of their reading-writing skills. The analysis is based on a questionnaire administered in 11 highly multilingual classrooms, in which at least 60% of the students spoke more than one language. The results show that, on the one hand, Italian is indicated as part of the linguistic repertoire by almost all the interviewees; on the other hand, the majority of the students reported to have high-level reading-writing skills in Italian, regardless of the composition of their linguistic repertoire. Finally, we present the preliminary results of a writing task in Italian that we administered to a sub-sample of students, in order to verify the impact of multilingualism on writing skills through the comparison between the written production of multilingual and monolingual speakers.
Exploring the Influence of Green Transparency Improvement on the Trust Repair after Greenwashing

The growing rise in demand and consumption of green products has led to firms increasingly utilising green brands to derive competitive advantage and brand benefits (Chen and Chang, 2013). However, the growing manifestation caused by greenwashing has made successful brands to lose their consumersâ€™ brand trust (Guo et al., 2018; Chen and Chang, 2013) as well as brand equity (Akturan, 2018; Chen, Tien, Lee and Tsai, 2016). This requires an effective brand strategy to be put forward to repair green brand trust after greenwashing (Guo et al., 2018). Previous research has explored different trust repair strategies including apologies (Pace, Fediuk and Botero, 2010), donations (Xia, Teng and Gu, 2019; Brown, Buchholtz and Dunn, 2016), denial (Fuoli, van de Weijer and Paradis, 2017), commitment (Schweitzer, Hershey and Bradlow, 2006), and excuses (Tomlinson and Mayer, 2009). Despite recent development of the prior literature, our understanding of trust repair mechanism after greenwashing is still limited (Guo et al., 2018). This study seeks to gain insights into the puzzle of organizational-level strategies for trust repair (green transparency improvement) and its influence on green brand authenticity, which plays a significant mediating role in the brand trust repair process. It contributes to the extant body of the literature by providing an authenticity perspective for understanding trust repair to green brands. Building on theory of organizational trust repair mechanism, our framework will explore the impact of green brand transparency on the green brand authenticity.
Creative and innovative teaching practices during COVID-19 Movement Control Order (MCO) / Jamal Othman, Azlina Mohd Mydin and Naemah Abdul Wahab

World Health Organisation (WHO) has declared COVID-19 as a pandemic disease. The number of infected people with COVID-19 has increased abruptly over the world. Many countries have declared lockdown and Movement Control Order (MCO) to control the transmission of the virus. The most affected ministry is the Education Ministry whereby all schools, colleges and universities have been instructed to close. Teachers and lecturers were advised to conduct their classes through non face to face or e-learning methods. Although it was sudden decision made by government and quite difficult to be faced or accepted by all instructors, everybody has to accept this is as the new norms. Several methods and technologies have been introduced to help the instructors delivering their lectures effectively to students. The instructors were also encouraged to make use of available technologies and implement free resources that are accessible from the Internet without incurring additional expenses so that the instructors and leaners are not burdened and stressed. Understanding the lectures delivered to the learners and effectiveness of the technology used are among the important elements in online teaching and learning (T&L) methods. This paper shares the experiences of several instructors applying the best practices of teaching and learning (T&L) technologies during the Movement Control Order (MCO) period.
J. HINTIKKA’S INTERROGATIVE EPISTEMOLOGY AND ITS POSSIBLE APPLICATIONS IN THE STUDY OF ARTIFICIAL INTELLIGENCE

In the article the possibility and prospects of using J. Hintikka’s interrogative epistemology for modeling cognitive activity in the process of developing artificial intelligence are substantiated.
Methodological features of the application of an integrative approach to the formation of metasubject results in teaching physics at school

In accordance with the requirements of the Federal State Educational Standard, the pupil must master the skills of transferring and integrating knowledge (meta-subject learning outcomes). Achievement of the indicated results is possible, including in extracurricular activities, organized in a special way. The article describes the levels of integration, highlights the requirements for the organization and content of extracurricular activities in physics based on an integrated approach, describes a model of methods for organizing extracurricular activities, gives examples of the implementation of interdisciplinary integration in teaching physics at school.
Secured Distributed Algorithms Without Hardness Assumptions.

We study algorithms in the distributed message-passing model that produce secured output, for an input graph $G$. Specifically, each vertex computes its part in the output, the entire output is correct, but each vertex cannot discover the output of other vertices, with a certain probability. This is motivated by high-performance processors that are embedded nowadays in a large variety of devices. In such situations, it no longer makes sense, and in many cases it is not feasible, to leave the whole processing task to a single computer or even a group of central computers. As the extensive research in the distributed algorithms field yielded efficient decentralized algorithms for many classic problems, the discussion about the security of distributed algorithms was somewhat neglected. Nevertheless, many protocols and algorithms were devised in the research area of secure multi-party computation problem (MPC or SMC). However, the notions and terminology of these protocols are quite different than in classic distributed algorithms. As a consequence, the focus in those protocols was to work for every function $f$ at the expense of increasing the round complexity, or the necessity of several computational assumptions. In this work, we present a novel approach, which rather than turning existing algorithms into secure ones, identifies and develops those algorithms that are inherently secure (which means they do not require any further constructions). This approach yields efficient secure algorithms for various locality problems, such as coloring, network decomposition, forest decomposition, and a variety of additional labeling problems. Remarkably, our approach does not require any hardness assumption, but only a private randomness generator in each vertex. This is in contrast to previously known techniques in this setting that are based on public-key encryption schemes.