Reviewerly/exHarmony huggingface dataset:
B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks

Urban areas are negatively impacted by Carbon Dioxide (CO 2 ) and Nitrogen Oxide (NO x ) emissions.In order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive emission allowances as needed.The current ETS is a centralized one, consisting of a set of complex rules.It is currently administered at the organizational level and is used for fixed-point sources of pollution such as factories, power plants, and refineries.However, the current ETS cannot efficiently cope with vehicle mobility, even though vehicles are one of the primary sources of CO 2 and NO x emissions.In this study, we propose a new distributed Blockchain-based emissions allowance trading system called B-ETS.This system enables transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-to-vehicle communication.In addition, we introduce an economic incentive-based mechanism that appeals to individual drivers and leads them to modify their driving behavior in order to reduce emissions.The efficiency of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity can lead to reduction of the emissions generated from those vehicles.We demonstrate that our method can be used for full life-cycle monitoring and fuel economy reporting.This leads us to conjecture that the proposed system could lead to important behavioural changes among the drivers.
Decolonizing HCI Across Borders

The HCI Across Borders (HCIxB) community has been growing in recent years, starting with the Development Consortium at CHI 2016 and the HCIxB Symposia at CHI since. This year, we intend to hold an HCIxB workshop that aims to foster the scholarship potential of student and early career HCIxB researchers across the world, particularly those from and in the Global South, engaging on the topic of decoloniality. Through this symposium, we aim to create space for discussions that have been emerging in pockets of the HCI community, but could benefit from greater focus and attention in the interest of demarginalizing members and research areas of the community that have thus far remained on the margins of the discipline. We expect this virtual workshop at CHI 2021 to be an inaugural session for a series of virtual events that will help continue this conversation on decolonizing HCI's borders.
Pattern-based solution for architecting cloud-enabled software

Cloud computing exploits the software as a service model with distributed and interoperable services for the composition of software systems. Cloud-enabled systems that demand elasticity, scalability, and composition of services, etc., there is a need to capitalize on reusable solutions exploiting patterns and styles to architect cloud-based software. The objective of this research is to build and exploit a catalog of patterns that support reusable design knowledge to develop cloud-based architectures. We propose a three-step process with (i) pattern discovery, (ii) pattern documentation (building the catalog), and finally, (iii) pattern application (exploiting the catalog) to enable pattern-based architecting of cloud systems. We discovered seven patterns as generic and reusable solutions and demonstrate the pattern-driven architecture of the ECMC case study. Results suggest that pattern-based architecting enables the reuse of generic design decisions but lacks fine-grained architectural design. The solution is the first attempt towards establishing the catalog as a repository of patterns for architecture-based development of cloud systems.
Asymptotic properties and stabilization of a neutral type system with constant delay

The problem of obtaining sufficient conditions for the asymptotic stability for a certain class of linear systems of a neutral type with constant delay is analyzed in the article. Some coefficients of these systems in the right side have an exponential factor. As a consequence, the study of the stability of such systems with the help of the Lyapunov—Krasovskii functionals is not possible; methods of receiving asymptotic appreciations lead to extremely rough results. By applying the apparatus of difference systems and the properties of simpler systems, which the author examined previous, sufficient conditions for the exponential stability of such systems are obtained. As an example, a second-order system is considered. The graphs of the solutions of the corresponding system, both without neutral members and with the original system where the right-hand side contains neutral terms, are provided. On the basis of theory difference systems, the author proposes an algorithm of stabilization for some systems of a similar type.
Reservoir Computing as a Tool for Climate Predictability Studies

&amp;lt;p&amp;gt;&amp;amp;#160; Reduced-order dynamical models play a central role in developing our&amp;lt;br&amp;gt;&amp;amp;#160; understanding of predictability of climate irrespective of whether&amp;lt;br&amp;gt;&amp;amp;#160; we are dealing with the actual climate system or surrogate climate&amp;lt;br&amp;gt;&amp;amp;#160; models. In this context, the Linear Inverse Modeling (LIM) approach,&amp;lt;br&amp;gt;&amp;amp;#160; by helping capture a few essential interactions between dynamical&amp;lt;br&amp;gt;&amp;amp;#160; components of the full system, has proven valuable in being able to&amp;lt;br&amp;gt;&amp;amp;#160; provide insights into the dynamical behavior of the full system.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160; We demonstrate that Reservoir Computing (RC), a form of machine&amp;lt;br&amp;gt;&amp;amp;#160; learning suited for learning in the context of chaotic dynamics,&amp;lt;br&amp;gt;&amp;amp;#160; provides an alternative nonlinear approach that improves on the LIM&amp;lt;br&amp;gt;&amp;amp;#160; approach. We do this in the example setting of predicting sea&amp;lt;br&amp;gt;&amp;amp;#160; surface temperature in the North Atlantic in the pre-industrial&amp;lt;br&amp;gt;&amp;amp;#160; control simulation of a popular earth system model, the Community&amp;lt;br&amp;gt;&amp;amp;#160; Earth System Model version 2 (CESM2) so that we can compare the&amp;lt;br&amp;gt;&amp;amp;#160; performance of the new RC based approach with the traditional LIM&amp;lt;br&amp;gt;&amp;amp;#160; approach both when learning data is plentiful and when such data is&amp;lt;br&amp;gt;&amp;amp;#160; more limited. The useful predictive skill of the RC approach over a&amp;lt;br&amp;gt;&amp;amp;#160; wider range of conditions---larger number of retained EOF&amp;lt;br&amp;gt;&amp;amp;#160; coefficients, extending well into the limited data regime,&amp;lt;br&amp;gt;&amp;amp;#160; etc.---suggests that this machine learning approach may have a use&amp;lt;br&amp;gt;&amp;amp;#160; in climate predictability studies. While the possibility of&amp;lt;br&amp;gt;&amp;amp;#160; developing a climate emulator---the ability to continue the&amp;lt;br&amp;gt;&amp;amp;#160; evolution of the system on the attractor long after failing to be&amp;lt;br&amp;gt;&amp;amp;#160; able to track the reference trajectory---is demonstrated in context&amp;lt;br&amp;gt;&amp;amp;#160; of the Lorenz-63 system, it is suggested that further development of&amp;lt;br&amp;gt;&amp;amp;#160; the RC approach may permit such uses of the new approach in settings&amp;lt;br&amp;gt;&amp;amp;#160; of relevance to realistic predictability studies.&amp;lt;/p&amp;gt;
Fuzzy-Marker-Based Segmentation Using Hierarchies

This article extends a classical marker-based image segmentation method proposed by Salembier and Garrido in 2000. In the original approach, the segmentation relies on two sets of pixels which play the role of object and background markers. In the proposed extension, the markers are not represented by crisp sets, but by fuzzy ones, i.e., functions of the image domain into the real interval [0, 1] indicating the degree of membership of each pixel to the markers. We show that when the fuzzy markers are indicator functions of crisp sets, the proposed method produces the same result as the original one. We present a linear-time algorithm for computing the result of the proposed method given two fuzzy markers and we establish the correctness of this algorithm. Additionally, we discuss possible applications of the proposed approach, such as adjusting marker strength in interactive image segmentation procedures and optimizing marker locations with gradient descent methods.
Blockchain Technology in Securitization in India

Blockchain and securitization's benefits go beyond the benefits of loan originating, underwriting, rating assignment and reviews, loan servicing, smart contracts, secondary market trading. This research is based on the qualitative data collected from external sources. The focus is on using secondary data from over 30 research papers, articles, and professional experiments that have been taken into account, and the formulas and conditions gathered from them have been experimented with the Indian economic scenario. The understanding is that securitization has always been a challenge in a country like India, but with the wide range of benefits from the ‘blockchain', it can help improve the challenges and ensure a wider scope of securitization in a country like India, emphasizing that blockchain gives an elevated level of information security which brings about lower ingenuity trouble and disposes of administrative failures from the cycle of securitization.
Efficient algorithms for decode efficient prefix codes

The cost of decompressing (decoding) data can be prohibitive for certain real-time applications. In many scenarios, it is acceptable to sacrifice (to some extent) on compression in the interest of fast decoding. We study anovel problem of finding a prefix tree having the best decode time under the constraint that the code length does not exceed a certain threshold for a natural class of memory access cost functions that use blocking (also referred to as lookup tables). We present exact and approximation algorithms for this problem that are based on dynamic programming and capitalize on interesting structures of the optimal solutions. The full version of this paper is available at [1]
Service-Level Agreement Negotiation in Cloud Computing Buying Organizations

The cloud archetype is fundamentally dynamic for both service customer and service provider. From the provider's perspective, resources are removed and/or added irregularly and urgently together with updating of service level agreements (SLAs). However, this dynamical behavior makes contracting sophisticated. Yet, there is a lack of knowledge on how to handle these dynamics contractually. To address this knowledge gap, the authors have investigated “how to improve SLA management with dynamic SLA iterations.” For this reason, a model for cloud computing SLA negotiation is proposed to facilitate the buying organization in determining uncertainty by analyzing attributes of asset specificity and risks associated with them. Survey research has been used for testing the model. The respondents rationalized the SLA negotiation with a better cognitive understanding in all phases within the model. The study's findings could support cloud service buying organizations to minimize the risks with ever-changing requirements.
{MIRAGE}: Mitigating Conflict-Based Cache Attacks with a Practical Fully-Associative Design

Shared processor caches are vulnerable to conflict-based side-channel attacks, where an attacker can monitor access patterns of a victim by evicting victim cache lines using cache-set conflicts. Recent mitigations propose randomized mapping of addresses to cache lines to obfuscate the locations of set-conflicts. However, these are vulnerable to new attacks that discover conflicting sets of addresses despite such mitigations, because these designs select eviction-candidates from a small set of conflicting lines. This paper presents Mirage, a practical design for a fully associative cache, wherein eviction candidates are selected randomly from all lines resident in the cache, to be immune to set-conflicts. A key challenge for enabling such designs in large shared caches (containing tens of thousands of cache lines) is the complexity of cache-lookup, as a naive design can require searching through all the resident lines. Mirage achieves full-associativity while retaining practical set-associative lookups by decoupling placement and replacement, using pointer-based indirection from tag-store to data-store to allow a newly installed address to globally evict the data of any random resident line. To eliminate set-conflicts, Mirage provisions extra invalid tags in a skewed-associative tag-store design where lines can be installed without set-conflict, along with a load-aware skew-selection policy that guarantees the availability of sets with invalid tags. Our analysis shows Mirage provides the global eviction property of a fully-associative cache throughout system lifetime (violations of full-associativity, i.e. set-conflicts, occur less than once in 10^4 to 10^17 years), thus offering a principled defense against any eviction-set discovery and any potential conflict based attacks. Mirage incurs limited slowdown (2%) and 17-20% extra storage compared to a non-secure cache.
Box4Rec: Box Embedding for Sequential Recommendation

Sequential recommendation aims to predict a user’s next behavior in near future by using the user’s most recent behaviors. Most of the existing methods always embed a user or an item as a point in a vector space, and then model the user’s recent behaviors as a sequence with a strict order to generate recommendations. However, both the point representation and strict order rule limit the capacity of sequential recommendation models as the diversity and uncertainty of a user’s interests. In this paper, by relaxing the condition that a sequence must follow a strict order, we introduce the box embedding into the sequential recommendation and present a novel model called Box4Rec. Box4Rec embeds a user and the user’s historical items as boxes instead of points to model the user’s general preference and short-term preference, and then integrates the conjunction and disjunction operations on items to generate flexible recommendation strategies. Experiments on five real-world datasets show the proposed Box4Rec model outperforms the state-of-the-art methods consistently.
Improvement in K-Means Clustering Using Variant Techniques

An algorithm that is applied to discover the clusters that can possibly be generated in a dataset is known as k-means clustering algorithm. The overview of improvements made in k-means clustering algorithm over the years is presented in this research. The results achieved as output from this algorithm in the form of number of clusters identified and their centroids are largely affected as per the selection of initial point. Several new methods have been designed to improve the performance of k-means clustering algorithm in terms of the computational time, performance and accuracy. Most of the improved techniques aim to reduce the computation time by minimizing the number of iterations being performed. It is seen through the studies that this algorithm is used commonly in several scenarios. A new hybrid algorithm that is highly efficient, accurate and consumes the least amount of time can be designed with the help of using previously proposed improvements in k-means algorithm.
A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.
TEACHER’ ASSESSMENT OF YOUNG LANGUAGE LEARNERS IN SPEAKING SKILL

ABSTRACT &#x0D; &#x0D; Young learners are learners who have unique that can imitate something like a language easily. Therefore, to know a language, they need an environment surrounded by people who speak the language that is targeted and meaningful because of the context. In this globalization era, many people agree that the English language is such an important subject that used to communicate globally. Many people assumed that Speaking is an important skill to mastering the language, however, it is very hard for beginners to speak a foreign language, especially English. In Indonesia, the English language has been taught at every level of learners, even in kindergarten and primary level. People believe that learning English at an early age has many advantages because young learners still have flexible minds and malleable tongues which help them learn English words easily. Furthermore, young learners are of course different from adult ones. Further, this paper aims to analyze how the teacher designing a test of speaking skills for young learners and how the teacher assesses young language learners in speaking skills. This research used a qualitative descriptive method and to obtain the data, the researchers used to interview and video documentation to gather the data. There were 17 teachers Madrasah Ibtidaiyah in Tajurhalang as participants. This research found that most of the teachers in Tajurhalang are used storytelling (8) and some of them also use picture talks (2), oral presentation (2) description (2), and also question and answer (3) as a technique that used by the teachers in assessing young learner speaking skill further, most of teachers Madrasah Ibtidaiyah in Tajurhalang district are use fluency (6) and some of them use comprehension (5), vocabulary (5), and pronunciation (1) as a criterion in assessing students speaking skill. Above all, as a language teacher, we have to know the characteristics of young learners and designing a test to make sure young learners' language speaking skill and components because it will be an important role for teaching and learning process to young learners better. &#x0D; &#x0D; Keywords: Young Learners, Speaking Skill, Designing Test
Computer Vision Techniques for Crowd Density and Motion Direction Analysis

A system using computer vision techniques for tracking and providing early information of hazardous locations in huge gatherings is the need of the hour. Also, the number of video streams generated are huge and are challenging to watch them personally. The proposed system is based on the Optical Flow based estimations and detects sequences of crowd motions that are characteristic for devastating congestions. Initially, the temporal features of the scenes are extracted using Motion History Image (MHI) technique. MHI technique involves the weighted subtraction of consecutive frames of the video stream. Then the Optical Flow vectors are calculated using the Lucas-Kanade method. Segmentation of Optical Flow fields is done, and hence directional histograms of motion magnitude against motion direction are determined for respective segments. Their entropy and absolute bin magnitudes characterize the graphs. Thresholds are chosen such that there is a demarcation between sparsely crowded and densely crowded segments in the frames. Localization of crowd density levels as unpopulated, sparse and dense helps in providing immediate attention to critical areas of congestion. The directional analysis helps in having knowledge of the significant direction of motion in sparsely crowded regions.
Development Overview of Augmented Reality Navigation

At present, with the increasing computing power of mobile phones, the wide application of AR technology in mobile phones has been realized.AR technology is not only used in mobile games, medical, video entertainment and other fields, but also become more and more popular in the perspective of navigation.This paper will firstly describe the development history of AR and related background knowledge, and focus on the application of AR technology in the field of navigation.Finally, it will discuss the development prospects and possible difficulties of AR+Navigation.
Cloud Computing for Malicious Encrypted Traffic Analysis and Collaboration

As the application of network encryption technology expands, malicious attacks will also be protected by encryption mechanism, increasing the difficulty of detection. This paper focuses on the analysis of encrypted traffic in the network by hosting long-day encrypted traffic, coupled with a weighted algorithm commonly used in information retrieval and SSL/TLS fingerprint to detect malicious encrypted links. The experimental results show that the system proposed in this paper can identify potential malicious SSL/TLS fingerprints and malicious IP which cannot be recognized by other external threat information providers. The network packet decryption is not required to help clarify the full picture of the security incident and provide the basis of digital identification. Finally, the new threat intelligence obtained from the correlation analysis of this paper can be applied to regional joint defense or intelligence exchange between organizations. In addition, the framework adopts Google cloud platform and microservice technology to form an integrated serverless computing architecture.
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving the F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively