Reviewerly/exHarmony huggingface dataset:
A Critical Review of the Current State of Natural Language Processing in Mexico and Chile

This chapter presents a critical review of the current state of natural language processing in Chile and Mexico. Specifically, a general review is made regarding the technological evolution of these countries in this area of research and development, as well as the progress they have made so far. Subsequently, the remaining problems and challenges are addressed. Specifically, two are analyzed in detail here: (1) the lack of a strategic policy that helps to establish stronger links between academia and industry and (2) the lack of a technological inclusion of the indigenous languages, which causes a deep digital divide between Spanish (considered in Chile and Mexico as their official language) with them.
Fuzzy Tuned PID Controller for Vibration Control of Agricultural Manipulator

Image-based phenotyping systems have evolved over the years, and become an integral part of crop and plant science research. Phenotyping systems provide great potential to deliver critical insights, than the conventional destructive field m ethods. S table i mage a cquisition a nd p rocessing i s very important to accurately determine the characteristics in general, which further becomes very challenging and non-trivial when mounted over an motor mechanised arm. To address the near associated problems, we investigate on the possibility of applying the Proportional-Integral-Derivative (PID) control algorithm to the present manual setup with an aim to reduce vibration. This study focused towards investigating the active control and stabilization of the external camera shake, that may be induced by the driving motor. Nonetheless, very few researchers have focused on application of control algorithms for agriculture related practices. We validate the active control, and justify the need for the same.Type-2 fuzzy logic is combined with the PID control for better effectiveness. The non-linearity associated with the system is compensated by the type-2 fuzzy logic. The results shows that the active control has been achieved, and the vibration is minimized.
Semantically Aware, Mission-Oriented (SAMO) Networks

With advances in programmable data plane, networks are far more flexible with configurable network functionality. At the same time, applications have also become increasingly complex, with diverse quality-of-experience requirements. We outline a Semantically Aware, Mission-Oriented (SAMO) networking framework for application-network integration. SAMO incorporates a per-packet, host-application-to-network-application signaling mechanism, enabling networks to quickly adapt to the changing network state in accordance with application semantics. It is designed for "unmanaged" environments, targeting many industrial Internet-of- Things (IoT) and military environments and applications.
Wrist-worn accelerometer based fall detection for embedded systems and IoT devices using deep learning algorithms

With increasing age, elderly persons are falling more often. While a third of people over 65 years are falling once a year, hospitalized people over 80 years are falling multiple times per year. A reliable fall detection is absolutely necessary for a fast help. Therefore, wristworn accelerometer based fall detection systems are developed but the accuracy and precision is not standardized, comparable or sometimes even known. In this paper, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly.
Have Your Tickets Ready! Impede Free Riding in Large Scale Team Assignments

Teamwork and graded team assignments in MOOCs are still largely under-researched. Nevertheless, the topic is enormously important as the ability to work and solve problems in teams is becoming increasingly common in modern work environments. The paper at hand discusses the reliability of a system to detect free-riders in peer assessed team tasks.
Clustering applications of IFDBSCAN algorithm with comparative analysis

Density Based Spatial Clustering of Application with Noise (DBSCAN) is one of the mostly preferred algorithm among density based clustering approaches in unsupervised machine learning, which uses epsilon neighborhood construction strategy in order to discover arbitrary shaped clusters. DBSCAN separates dense regions from low density regions and simultaneously assigns points that lie alone as outliers to unearth the hidden cluster patterns in the datasets. DBSCAN identifies dense regions by means of core point definition, detection of which are strictly dependent on input parameter definitions: ε is distance of the neighborhood or radius of hypersphere and MinPts is minimum density constraint inside ε radius hypersphere. Contrarily to classical DBSCAN’s crisp core point definition, intuitionistic fuzzy core point definition is proposed in our preliminary work to make DBSCAN algorithm capable of detecting different patterns of density by two different combinations of input parameters, particularly is a necessity for the density varying large datasets in multidimensional feature space. In this study, preliminarily proposed DBSCAN extension is studied: IFDBSCAN. The proposed extension is tested by computational experiments on several machine learning repository real-time datasets. Results show that, IFDBSCAN is superior to classical DBSCAN with respect to external &amp; internal performance indices such as purity index, adjusted rand index, Fowlkes-Mallows score, silhouette coefficient, Calinski-Harabasz index and with respect to clustering structure results without increasing computational time so much, along with the possibility of trying two different density patterns on the same run and trying intermediary density values for the users by manipulating α margin.
Emotions Behind Drive-by Download Propagation on Twitter

Twitter has emerged as one of the most popular platforms to get updates on entertainment and current events. However, due to its 280-character restriction and automatic shortening of URLs, it is continuously targeted by cybercriminals to carry out drive-by download attacks, where a user’s system is infected by merely visiting a Web page. Popular events that attract a large number of users are used by cybercriminals to infect and propagate malware by using popular hashtags and creating misleading tweets to lure users to malicious Web pages. A drive-by download attack is carried out by obfuscating a malicious URL in an enticing tweet and used as clickbait to lure users to a malicious Web page. In this article, we answer the following two questions: Why are certain malicious tweets retweeted more than others? Do emotions reflecting in a tweet drive virality? We gathered tweets from seven different sporting events over 3 years and identified those tweets that were used to carry to out a drive-by download attack. From the malicious ( N = 105, 642) and benign ( N = 169, 178) data sample identified, we built models to predict information flow size and survival. We define size as the number of retweets of an original tweet, and survival as the duration of the original tweet’s presence in the study window. We selected the zero-truncated negative binomial (ZTNB) regression method for our analysis based on the distribution exhibited by our dependent size measure and the comparison of results with other predictive models. We used the Cox regression technique to model the survival of information flows as it estimates proportional hazard rates for independent measures. Our results show that both social and content factors are statistically significant for the size and survival of information flows for both malicious and benign tweets. In the benign data sample, positive emotions and positive sentiment reflected in the tweet significantly predict size and survival. In contrast, for the malicious data sample, negative emotions, especially fear, are associated with both size and survival of information flows.
A multiresolution wavelet optimised finite-difference method for simulation of thermal regenerator

The equations demonstrating the heat transfer phenomenon in a thermal regenerator were solved with the finite-difference method (FDM) and the method has been improvised by using wavelets. In the thermal regenerators, hot flue gas enters an initially cold bed, transfer all of its thermal energy in a portion of the bed and leaves at low temperature creating a heat transfer zone which traverses the bed-length with time while establishing the thermal equilibrium on both sides of the zone. The discretization process in conventional FDM takes uniform dense grids for whole domain, however, in actual physical process, the heat transfer happens only in a portion of the domain at a certain time requiring dense grids. Such problems demand a front-tracking method which can adjust the grid pattern according to the temperature-front location for which wavelets are found to be suitable. Wavelets functions have the property of being translated and dilated according to the requirement. An adaptive grid scheme has been developed using wavelets along with the multiresolution analysis. The grid patterns for different forms of Daubechies wavelets i.e. db1, db2, db3, and db4 have been compared. The method based on wavelets has been found accurate and less time-consuming.
Forward-Secure 0-RTT Goes Live: Implementation and Performance Analysis in QUIC

Modern cryptographic protocols, such as TLS 1.3 and QUIC, can send cryptographically protected data in “zero round-trip times (0-RTT)”, that is, without the need for a prior interactive handshake. Such protocols meet the demand for communication with minimal latency, but those currently deployed in practice achieve only rather weak security properties, as they may not achieve forward security for the first transmitted payload message and require additional countermeasures against replay attacks. Recently, 0-RTT protocols with full forward security and replay resilience have been proposed in the academic literature. These are based on puncturable encryption, which uses rather heavy building blocks, such as cryptographic pairings. Some constructions were claimed to have practical efficiency, but it is unclear how they compare concretely to protocols deployed in practice, and we currently do not have any benchmark results that new protocols can be compared with. We provide the first concrete performance analysis of a modern 0-RTT protocol with full forward security, by integrating the Bloom Filter Encryption scheme of Derler et al. (EUROCRYPT 2018) in the Chromium QUIC implementation and comparing it to Google’s original QUIC protocol. We find that for reasonable deployment parameters, the server CPU load increases approximately by a factor of eight and the memory consumption on the server increases significantly, but stays below 400 MB even for medium-scale deployments that handle up to 50K connections per day. The difference of the size of handshake messages is small enough that transmission time on the network is identical, and therefore not significant. We conclude that while current 0-RTT protocols with full forward security come with significant computational overhead, their use in practice is feasible, and may be used in applications where the increased CPU and memory load can be tolerated in exchange for full forward security and replay resilience on the cryptographic protocol level. Our results serve as a first benchmark that can be used to assess the efficiency of 0-RTT protocols potentially developed in the future.
Coronavirus and the carnivalesque

column Share on Coronavirus and the carnivalesque: what speculative methods can tell us about Covid-19 Author: Cally Gatehouse Northumbria University Northumbria UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 4July - August 2020pp 34–36https://doi.org/10.1145/3403888Published:09 July 2020Publication History 4citation617DownloadsMetricsTotal Citations4Total Downloads617Last 12 Months78Last 6 weeks28 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteGet Access
Stitch: the sound type-indexed type checker (functional pearl)

A classic example of the power of generalized algebraic datatypes (GADTs) to verify a delicate implementation is the type-indexed expression AST. This functional pearl refreshes this example, casting it in modern Haskell using many of GHC's bells and whistles. The Stitch interpreter is a full executable interpreter, with a parser, type checker, common-subexpression elimination, and a REPL. Making heavy use of GADTs and type indices, the Stitch implementation is clean Haskell code and serves as an existence proof that Haskell's type system is advanced enough for the use of fancy types in a practical setting. The paper focuses on guiding the reader through these advanced topics, enabling them to adopt the techniques demonstrated here.
$$\mathbb {K}$$—A Semantic Framework for Programming Languages and Formal Analysis

We give an overview on the applications and foundations of the $$\mathbb {K}$$ language framework, a semantic framework for programming languages and formal analysis tools. $$\mathbb {K}$$ represents a 20-year effort in pursuing the ideal language framework vision, where programming languages must have formal definitions, and tools for a given language, such as parsers, interpreters, compilers, semantic-based debuggers, state-space explorers, model checkers, deductive program verifiers, etc., can be derived from just one reference formal definition of the language, which is executable, and no other semantics for the same language should be needed. The correctness of the language tools is guaranteed on a case-by-case basis by proof objects, which encode rigorous mathematical proofs as certificates for every individual task that the tools do and can be mechanically checked by third-party proof checkers.
Gap Based Novel Approach for Safe and Fast Obstacle Avoidance for Autonomous Platforms

Obstacle avoidance methods play a vital role in robotic navigation tasks. In this work, we are proposing a novel obstacle avoidance framework for mobile robots equipped with 2d lidars. This method is inspired by previously proposed methods: FGM, SND and their variants. In this work, FGM-I was used as a base. FGM-I was selected for its easy applicability and adequate performance on moderate problems. However, there are certain drawbacks associated with the use of this algorithm in cluttered environments. This method suffers from narrow openings and not having a collision checking procedure. To increase the navigation safety and control robustness, proposed FGM-I2 method uses extra avoidance functions and control procedures. The results of this study indicate that according to overall results, FGM-I2 outperforms four different FGM and SND variants when evaluated w.r.t safety and speed.
Evaluation of Low-end Virtual Reality Content of Cultural Heritage

The affordances of virtual reality (VR) have made it widely adopted for presenting content of cultural heritage in digital libraries. In recent years, non-specialists including university students were involved in creating low-end VR content using low-cost equipment and software. Among various design options, the question as to which ones could be more effective in presenting cultural heritage remains. This study aims to evaluate and compare the effectiveness of user-created VR content of cultural heritage with different designs, through collecting and analyzing self-report and eye movement data from end users. Results show that the presence of text annotations in the VR content helped users understand the cultural heritage being presented, whereas users' visual attention was largely attracted to the text annotations and additional images when the VR content contained such visual information. This preliminary study also explores the feasibility of using the eye-tracking method to analyze user interactions with VR content of cultural heritage. The results provide empirical evidence on the effects of different designs of user-created VR content on end users' understanding of cultural heritage.
Out of control? Using STAMP to model the control and feedback mechanisms surrounding identity crime in darknet marketplaces

Darknet marketplaces have emerged as a facilitator of identity crime and trading. This study aimed to (1) understand the entities and control and feedback mechanisms that influence identity crime prevention and occurrence on the darknet in the Australian system and to (2) comprehensively identify the implications of control failures across all system levels. The Systems-Theoretic Accident Model and Processes (STAMP) was used to develop an identity crime control structure in consultation with subject matter experts and then the Systems-Theoretic Process Analysis (STPA) was applied. The STPA identified 310 risk states, resulting from control failures and which were associated with the range of agencies, organisations, and individuals present across system levels. As darknet marketplaces rapidly evolve, alignment between these entities is necessary to enable agile system responses. STAMP and STPA have promise in understanding the potential for intervention across all system levels in preventing societal issues such as identity crime.
Conceptual Online Education Using E-Learning Platform of Cloud Computing

Education is the process of procurement of knowledge; accelerating learning, adroitness, worth, credence, and custom. An e-learning platform is built with a goal to provide knowledge to everyone without making them physically attain it and also at a minimum cost without any hassle. e-Learning presents solutions that include Learning Management Systems(LMS) integration, online courses, evaluation engines, and test prep systems on diverse distribution channels such as the Internet and mobile apps with rapidly evolving digital revolution and smartphone growth. Today’s e-learning platform requires a huge cost to set up their required resources and various applications related to software. The acceptance of cloud architecture and its computing can help them to reduce expenditure on resources required for the setup of infrastructure, software, and human assets to an extensive level. The paper highlights important aspects of the present e-learning framework and its related models along with issues in present applications of e-learning. The paper also throws light on cloud computing principles and analyzes benefits for adopting them. A solution for applications related to e-learning using cloud computing is proposed herewith.
Apriori-based Spatial Pattern Mining Algorithm for Big Data

With the establishment of recent information system networks, maritime vessel trajectory data are becoming increasingly available. Spatial pattern mining in these data can measure the behaviour of ships in space and time and can be used in traffic monitoring or other security purpose. The difficulty of this topic is that it needs to deal with both highdimensional data and huge amount of data.In this paper, a cooccurrence pattern mining algorithm based on Mapreduce architecture is presented. The algorithm is focused on the mining on spatial co-occurrence pattern, and implemented in the parallel partitioning architecture. The experiments on real data sets show that the large-scale ship trajectory data can be processed effectively.
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control

This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some of these dimensions may include redundant information in the dissimilarity representation space generated by the dichotomy transformation (DT) used by the writer-independent (WI) approach. The analysis is carried out on the GPDS-960 dataset. Experiments demonstrate that the proposed method is able to control overfitting during the search for the most discriminant representation.
Towards Crowdsourcing the Identification of Knowledge Components

Assigning a set of hypothesized knowledge components (KCs) to assessment items within an ed-tech system enables us to better estimate student learning. However, creating and assigning these KCs is a time-consuming process that often requires domain expertise. In this study, we present the results of crowdsourcing KCs for problems in the domain of mathematics and English writing, as a first step in leveraging the crowd to expedite this task. Crowdworkers were presented with a problem and asked to provide the underlying skills required to solve it. Additionally, we investigated the effect of priming crowdworkers with related content before having them generate these KCs. We then analyzed their contributions through qualitative coding and found that across both the math and writing domains roughly 33% of the crowdsourced KCs directly matched those generated by domain experts for the same problems.
KShot: Live Kernel Patching with SMM and SGX

Live kernel patching is an increasingly common trend in operating system distributions, enabling dynamic updates to include new features or to fix vulnerabilities without having to reboot the system. Patching the kernel at runtime lowers downtime and reduces the loss of useful state from running applications. However, existing kernel live patching techniques (1) rely on specific support from the target operating system, and (2) admit patch failures resulting from kernel faults. We present KSHOT, a kernel live patching mechanism based on x86 SMM and Intel SGX that focuses on patching Linux kernel security vulnerabilities. Our patching processes are protected by hardware-assisted Trusted Execution Environments. We demonstrate that our technique can successfully patch vulnerable kernel functions at the binary-level without support from the underlying OS and regardless of whether the kernel patching mechanism is compromised. We demonstrate the applicability of KSHOT by successfully patching 30 critical indicative kernel vulnerabilities.