Reviewerly/exHarmony huggingface dataset:
Enhancement of Reading Visibility using LED Embedded Whiteboards

As we're running towards IOT at a greater pace, we've ignored many things behind. The latest smartboards that we currently have in the market are very costly as they come with the latest technologies. It is not possible to have those smartboards in every place as they come with a great price. The small schools and institutions are lagging behind as they are still using as they are still using blackboards which are not good for health. The normal whiteboards work best in good lighting conditions which is not available everywhere especially in small town and villages. Moreover, studying in low lighting is harmful for the eyes of developing children. The idea is to combine white LEDs with normal whiteboards so as to improve the visibility of the text. We know that LEDs consume very less electricity that would allow small villages and institutions to use it. This will help in good visibility even in low light conditions. That would allow users with Myopia to read the text properly. And this would also prevent the defect to develop in the students.
Research on Strategy of Keeping Multi-UAVs Flight Formation Based on Distribution Method

Concerning the strategy of keeping multi-UAVs flight formation based on distribution method, first, large-scale multi-UAVs formation is divided into numerous two-UAV sub-formations in the way of “hierarchy”. Then, lead and wing aircraft control approach is adopted in sub-formations, i.e., UAVs in the formation are classified into lead and wing aircrafts, which are complementary to each other. In this way, formation control is thus realized by controlling wing aircrafts that track the lead aircraft. The close connection of sub-formations also constitutes a tracking chain–UAV i tracks UAV i-1. Finally, multi-UAVs formation flight is realized.
A Semantic Notion of Secure Information-Flow

It is highly desirable to have a safety property characterizing a general notion of secure information flow that succinctly captures the underpinnings of language-based security for general purpose programming languages. Such a notion must necessarily consider both implicit and explicit flows, and the need to provide labeled outputs. The notion must also embed access control and information-flow control considering that the program output is usually a set of multilevel labeled outputs. Finally, it must be based on a notion of security violations, as the latter plays a vital role in compositionality of security properties. The widely used notion of non-interference does not meet many of these criteria, and its use has crippled the progress of secure programming systems and operating systems. A notion of security that takes the desired criteria into account is proposed, and its advantages in comparison with non-interference and other related notions is established. We further relate our notion to the early works of Fenton and Denning to highlight its succinctness in defining language-based security, and also with that of Boudol’s notion of secure information flow as a safety property. An added advantage of our approach compared to Boudol’s approach is that it generalizes to non-deterministic systems and systems with rich constructs such as exceptions etc.
Application of Complex Continuous Wavelet Analysis to Auditory Evoked Brain Responses

Most infants learn words by hearing. Hearing tests for infants are very important because infants who are hard of hearing often need special training to learn language skills. Unfortunately, hearing tests take 30 min, during which time the infant must often be under anesthesia. Therefore, reducing the test time would be beneficial. This paper proposes the use of a new hearing test based on the complex continuous wavelet analysis as a possible faster alternative.
Event Observation of Date-time Stamps for ADS Reconstruction

Alternate Data Streams (ADS) is a method of information hiding that is only possible on NTFS file systems. Criminals are using it to hide data because the ADS can hide any size and type of data in NTFS file system. ADS is invisible to users. However, ADS operation updates the temporal attribute of cover medium, which could be a trace for ADS evaluating. Cover medium indicates to the file/folder which is used for ADS operation (creating, modifying and overwriting). In general file/folder operation, if we create (archive/copy) a file into folder, it updates certain timestamps attributes of folder and file itself. Same result takes place when modifying/overwriting the file within folder. Based on this concept, we took a file within folder as a cover medium and applied some operations (create, modify and overwrite) on it to observe the timestamps variation on both folder and cover medium.
Linearization Error Analysis of Observation Equations in Pseudo Satellite Positioning System

For the problem that the observation environment is poor and the positioning accuracy is not high, pseudolites can be used for positioning. However, compared with navigation satellites, pseudolites are much closer to user receivers. Using algorithms such as EKF may cause relatively large linearization errors, but UKF algorithm can avoid linearization errors. Through theoretical analysis, it is found that the distance between the satellite and the receiver of the user and the coordinate error of the user receiver will affect the linearization error. In this paper, We used different receiver coordinate errors, and a set of short-baseline pseudo-satellite positioning experimental data was solved by UKF algorithm and EKF algorithm to verify the influencing factors of linearization error. The experimental results show that the linearization error is larger when the receiver coordinate error is larger or the satellite distance from the receiver is smaller. When the receiver coordinate error is relatively large, the convergence speed and overall accuracy of the UKF algorithm are better than EKF algorithm.
Towards data variation trends recommendation

Present study on recommender systems mainly focuses on the logical nature of the existence or non-existence of a priority relationship between the user and data item, regardless of the ratio or implicative relationship based on statistics between users and data items in a particular context. Therefore, this report proposes a new approach to recommender systems based on data variation trends; such method will help form a new approach to recommender systems on basis of knowledge available in the form of implicity by computation of partial derivatives for interestingness measurements. In addition, experiments aim at evaluating the effectiveness of the proposed model with traditional models based on using MSWeb dataset as empirical data, comparing and discussing the results obtained from the proposed model.
Controlled quantum teleportation on the three control pattern via five-qubit cluster state

We proposed a new scheme of controlled teleportation to transmit the arbitrary one qubit state information via five-qubit cluster state channel. The transmission of quantum information from Alice (A) to Charli (C) had been controlled by Bob (B). The distribution of local measurements on Alice's, Bob's and Charli's qubit studied at three patterns; there are AAABC, ABABC, and ABBBC. The important results of this research are the observed differences in output variation and the efficiency of sending arbitrary one qubit information to the control pattern on Bob's Particle via five-qubit cluster state channel.
RETRACTED: Deeply fusing multimodal features in hypergraph

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Journal of Visual Communication and Image Representation was made aware that the paper “Deeply fusing multimodal features in hypergraph” was previously published in Multimedia Tools and Applications, 2017, https://doi.org/10.1007/s11042-017-5305-6.The content was submitted to Journal of Visual Communication and Image Representation without disclosure of its previous publication. As such this article represents a misuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process. The authors have not responded to correspondence about this retraction.
Can off-task be on-track?

Collaborative learning requires a lot of talk. Although not all student talk may be related to the task at hand, some off-task talk is actually productive, as it enables students to negotiate how they will work together, gain attention of fellow group members, and draw others into joining the work. Emma C. Gargroetzi, Rosa D. Chavez, Jen Munson, Jennifer M. Langer-Osuna, and Kimiko E. Lange observed 4th-grade students working in groups on math exercises and saw multiple seemingly off-task conversations that, in fact, ended up bringing the group together. For example, students who were excluded used off-task talk to get others in the group to pay attention to them. Groups also used off-task talk to ensure that everyone in the group had a role in the solution. The authors offer guidelines for determining when to intervene when students engage in off-task talk.
Improved analysis of the reduction from BDD to uSVP

In ICALP 2016, Bai, Stehlé and Wen showed that for polynomially bounded parameter γ, the Bounded Distance Decoding (BDD) problem with parameter 1/(2γ) can be probabilistically reduced to the unique Shortest Vector Problem (uSVP) with parameter γ. Their reduction is based on the lattice sparsification and embedding techniques. In this paper, we improve their analysis for specific values of the parameter and prove that BDD1/(2γ) can be reduced to uSVPγ1 with a slightly larger gap. The improvement follows from a careful study of the radius of a sphere which contains at most polynomial number of vectors, then a proper choice of the parameter in the embedding lattice is used to obtain a unique lattice with a larger gap. Our result shows that the equivalence between BDD1/(2γ) and uSVPγ does not hold under the assumption that uSVPγ does not reduce to uSVPγ′ for any two constants γ<γ′.
HasAnswers

Many young people experience difficulty finding and keeping their first independent home, which can lead to homelessness or risk of homelessness. To help address this challenge, a young people's service in Scotland (Calman Trust) is developing a digital tool called HasAnswers. This paper provides a brief description of HasAnswers, the results of iterative testing with 69 young people (40 male, 29 female) using paper and digital prototypes, and feedback from other services with a responsibility for supporting young people to achieve an independent adulthood, as a potential customer base for the future scaling up of HasAnswers to new geographical locations and organizations. While preliminary, the results/feedback has been consistently to confirm the potential usefulness and acceptability of HasAnswers. Next steps include applying the results of the latest user testing followed by pilot testing. The research contributes to the body of work within HCI on design for homelessness by providing a new digital tool with a greater emphasis on prevention and early intervention, informed by an iterative user-centred design process.
Virtual Reality in Visual Analytics of Large Datasets

Visual analytics can be defined as a representation of data in form of diagrams, charts, pictures, graphs, etc., whereas virtual reality is a term used for the simulated interactive environment that exploits multiple sense organs of human beings to perceive information. Both of these techniques are merged to create an interactive environment for data visualization and analysis. Often it happens that a large volume of data is complex to represent, so to represent large, congested, and complex data in a manageable and comprehensive form, visual analytics is the need of an hour. The chapter discusses the scope of visual analytics, the role of virtual reality in visual analytics, challenges in VA using VR, tools used to implement it, use, and applications.
Machine Learning for Land Classification - A SOM Case Study of Broken Hill

PreviousNext No AccessProceedings of the 13th SEGJ International Symposium, Tokyo, Japan, 12–14 November 2018Machine Learning for Land Classification - A SOM Case Study of Broken HillAuthors: Tasman Gillfeather-ClarkLuke SmithTasman Gillfeather-ClarkUniversity of Western AustraliaSearch for more papers by this author and Luke SmithMacquarie UniversitySearch for more papers by this authorhttps://doi.org/10.1190/SEGJ2018-138.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Machine learning (ML) has come to refer to a diverse range of algorithms and functions designed with the intent of learning a given problem and producing some evaluable result, as opposed to being explicitly programmed to solve said problem. Typically, they are iterative, and improve their conclusions over time. The definition commonly introduced is paraphrased from the work of Samuel (1959), who worked on some of the earliest examples of ML, by applying what we now refer to as a decision tree to teach a computer to play checkers. The usage of ML in the geoscience industry has the potential to become an indispensable tool for geoscientists in all stages of the exploration process. As the mining and resource evaluation industry faces an ever-growing data glut, ML presents a range of tools to work with increasingly large, multivariate datasets. Self-Organising Maps (SOM) is an unsupervised learning algorithm, used in this work to complete landmass classification analysis of the area to the North of Broken Hill. An examination of current ML landmass classification methodologies is introduced followed by a brief review of SOM. Applications of SOM for mineralisation targeting and data QC are identified in a data rich setting. The results of the study confirm the efficiency of the SOM algorithm for clustering lithological groups in land classification studies. Perhaps most notable is SOM's ability to highlight variation in cover without needing to assign labels, which has been identified as a key aspect moving forward in Australia's mining future, when considering the vast expanses of Australia which is composed of sub cropping rock. Keywords: SOM, Machine Learning, Data Integration, Broken Hill, Remote SensingPermalink: https://doi.org/10.1190/SEGJ2018-138.1FiguresReferencesRelatedDetails Proceedings of the 13th SEGJ International Symposium, Tokyo, Japan, 12–14 November 2018ISSN (online):2159-6832Copyright: 2019 Pages: 588 publication data© 2018 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Apr 2019 CITATION INFORMATION Tasman Gillfeather-Clark and Luke Smith, (2019), "Machine Learning for Land Classification - A SOM Case Study of Broken Hill," SEG Global Meeting Abstracts : 534-537. https://doi.org/10.1190/SEGJ2018-138.1 Plain-Language Summary KeywordsSOMMachine LearningData IntegrationBroken HillRemote SensingPDF DownloadLoading ...
Traffic Sign Detection Using R-CNN

Due to the increasing popularity of driving assistance systems, traffic sign detection has gained increased attention due to its role in traffic safety. This paper presents a traffic sign detection algorithm based on the regions with convolutional neural network (R-CNN) method. In this method, a segmentation algorithm proposes regions likely to contain a traffic sign. These regions are analyzed by a convolutional neural network providing a feature vector to a support vector machine. This method, however, suffers from a high computation time due to an excessive amount of regions proposed. To reduce the number of regions, this work proposes a perspective based logic that discards regions unlikely to contain traffic signs because of a size-position-mismatch. This results in an average 93% reduction of proposed regions. For faster training, the system consists of a two-layer support vector machine. The first layer is used to categorize the family of the picture, the second set of SVMs for classifying the traffic sign. The overall classification accuracy of this technique is 86%. Feeding the results to a tracking algorithm may result in an overall improvement because frames would compensate errors.
Exploiting non-uniform program execution time to evade record/replay forensic analysis

Record/replay system is an essential and widely used module in forensic analysis, as it can help forensic analysts to reconstruct programs’ behaviors. However, the security implication of record/replay systems (i.e., whether record/replay systems can faithfully reproduce all behaviors of a program) has not been thoroughly studied. This paper is the first work which investigates and explores the security limitations of record/replay systems from the perspective of software forensics. In particular, we reveal a type of vulnerability in record/replay systems caused by non-uniform program execution time. A program can exploit this vulnerability to prevent its malicious behavior from being replayed. We conduct a series of experiments on three platforms (i.e., web browser, mobile operating system and virtualized sandbox) to illustrate the wide footprints of the vulnerability. Finally, we discuss possible methods to mitigate the vulnerability. The goal of this work is to study the inherent security limitations of record/replay systems, discover the vulnerability and explore potential mitigation methods, from which forensic analysts can be informed and cautious when applying record/replay systems to software forensics.
Models of Minds

Drawing on philosophies of embodied, distributed & extend cognition, this paper argues that the mind is readable from sensors worn on the body and embedded in the environment. It contends that past work in HCI has already begun such work, introducing the term models of minds to describe it. To those who wish to develop the capacity to build models of minds, we argue that notions of the mind are entangled with the technologies that seek to sense it. Drawing on the racial and gendered history of surveillance, we advocate for future work on how models of minds may reinforce existing vulnerabilities, and create new ones.
PSpec-SQL: Enabling Fine-Grained Control for Distributed Data Analytics

Business organizations regularly collect customer data to improve their services. Organizations may want to share data within themselves or even with third-parties to maximize data utility. Since business data contain lots of customer data, organizations must respect customers' privacy expounded by privacy laws. In this paper, we present PSpec-SQL, a distributed data analytics system that automatically enforces privacy compliance for SQL queries. Our system provides a high-level language PSpec for the data owner to specify her data usage policy. As usual, the data analyst queries data to perform data analysis, but our system checks each query to ensure only policy-compliant queries are executed. We have implemented a prototype of PSpec-SQL on top of Spark-SQL, and carried out a case study on the TPC benchmarks. The results show the practicability of our system with negligible overhead over query processing.
A Pairing-Based Three-Party Authenticated Encryption Scheme without Shared Secrets

The Traditional Authenticated Encryption (AE) scheme is a single-user cryptographic mechanism which only enables one designated verifier to authenticate the ciphertext. Although several group-oriented AE variants have also been proposed to eliminate such a limitation, they require shared verification. This motivated us to think of a scenario of three-party communication environments where each party runs independent processes without cooperation. In this paper, we realize a novel three-party AE (abbreviated to TPAE) scheme in which two designated verifiers can solely decrypt the same ciphertext and then inspect the validity of embedded signature. Additionally, we also show that our TPAE construction is computationally secure using the well-defined IND-CCA2 and the EF-CMA adversary games in the proof model of random oracles. The comparison results will demonstrate the computational efficiency of our mechanism.
Linear Hashing Is Awesome

We consider the hash function hpxq " ppax `bq mod pq mod n where a, b are chosen uniformly at random from t0, 1, . . ., p ´1u.We prove that when we use hpxq in hashing with chaining to insert n elements into a table of size n the expected length of the longest chain is Õ`n 1{3 ˘.The proof also generalises to give the same bound when we use the multiply-shift hash function by Dietzfelbinger et al. [
Multimodal Interface for Drawing Diagrams that Does not Interfere with Natural Talking and Drawing

The aim of this research is to support real-time drawingin talking by using multimodal user interface technologies. In this situation, if talking and drawing are considered as commands by mistake during presentation, it will disturb users' natural talking and drawing. To prevent this problem, we introduce two modes of a command mode and a free mode, and explore smooth mode switching techniques that does not interfere with users' natural talking and drawing. We evaluate four techniques. Among them, a technique that specifies the command mode after actions using a pen gesture was the most effective. In this technique, users could quickly draw diagrams, and specifying mode switching didn't interfere with users' natural talk.
Live Demonstration: Bringing Powerful Deep Learning into Daily-Life Devices (Mobiles and FPGAs) Via Deep k-Means

The record-breaking success of convolutional neural networks (CNNs) comes at the cost of a large amount of model parameters. The resulting prohibitive memory storage and data movement energy have been limiting the extensive deployment of deep learning on daily-life edge devices which usually have limited storage capability and are battery-powered. To this end, we explore the employment of a recently published weight clustering technique, called deep k-Means which makes use of the redundancy within CNN parameters for reduced memory storage and data movement, and demonstrate k-Means's effectiveness in the context of an interactive real-time object detection using three representative daily-life devices (iPhone, iPad and FPGA).
A case-based approach using phonological knowledge for identifying error patterns in children's speech

Case-Based Reasoning (CBR) covers a range of different methods for organizing, retrieving, and indexing knowledge from previous cases. Thus, this methodology has been successfully applied in medical domain, due to its human and intelligent properties to diagnose the case of a patient. In the speech therapy domain, an early identification of speech sound disorders allows the diagnosis and treatment of various pathologies and may aid clinical decision-making. However, there are few proposals that use knowledge modeling for supporting speech therapists. Moreover, there is no indicative in related literature of CBR being used for detecting the phonological processes (PPs) that may occur in pronunciations. So, in this paper, we present a case-based approach that uses machine learning for predicting PPs, aiming to provide clinical support in the identification of error patterns in children's speech. The method was evaluated through a speech corpus containing near one hundred thousand audio files, collected from pronunciation assessments performed by speech-language pathologists with more than 1,000 children. Using our knowledge base along with incremental learning, we obtained an accuracy of over 93% for predicting the PPs, showing the efficiency of our method for clinical decision support.