Reviewerly/exHarmony huggingface dataset:
Learned Dictionaries-Based Watermarking for Speech Authentication

Robust watermarking have been widely investigated for solving the problems of speech integrity authentication. In this paper, we introduce a novel robust watermarking algorithm for speech integrity authentication. In the watermarking algorithm, for embedding robustness, the approximation coefficients for stationary wavelet transforms are modified by patchwork and quantization index modulation, the robustness is further obtained through learning the approximation coefficients under learned dictionaries. The efficiency of the watermarking algorithm is shown via several experimental results. The good inaudible is first shown by signal-to-noise ratio and objective difference grades. Then, the robustness is shown by against various common signal processing operations whereas keeping inaudibility. The proposed scheme is sensitive to malicious modification of speeches, and simultaneously tolerate some common signal processing. Compared with state-of-the-art algorithms, the proposed algorithm obtains a better comprehensive performance in robustness.
Co-clustering for binary and functional data

Due to the diversity of mobile network technologies, the volume of data that has to be observed by mobile operators in a daily basis has become enormous. This huge volume has become an obstacle to mobile networks management. This paper aims to provide a simplified representation of these data for an easier analysis. A model-based co-clustering algorithm for mixed data, functional and binary, is therefore proposed. Co-clustering aims to identify block patterns in a dataset from a simultaneous clustering of rows and columns. The proposed approach relies on the latent block model, and three algorithms are compared for its inference: stochastic EM within Gibbs sampling, classification EM and variational EM. The proposed model is the first co-clustering algorithm for mixed data that deals with functional and binary features. The model has proven its efficiency on simulated data and on real data extracted from live 4G mobile networks.
Variable that influence achievement of indonesian students in the program international student assessment (PISA) 2015 using structural equation modelling (SEM)

Abstract The Program for International Student Assessment (PISA) is an examination system that evaluates the education system every 3 years to 15-year-old students randomly selected. Indonesia ranking was generally still below the average of the Organisation for Economic Cooperation and Development (OECD). In addition, the results of the PISA study are not only interpreted as ranking, but also important to carry out follow-up based on diagnoses produced from the PISA diagnostic survey to determine the weaknesses and strengths of Indonesian students and evaluate the education system in Indonesia. It is important to consider the use of the right and simple model to see the relationship between the diagnostic results of the performance of students’ PISA achievements in 2015. The purpose of this study was to select variables that significantly influence PISA achievement in 2015 using the SEM regularization model using The Least Absolute Shrinkage and Selection Operator (LASSO) method. The results of the analysis show that the variables that significantly effects students’ PISA results are the students’ insight into the environmental, the students’ perceptions of science truth, and the students’ participation in The International Standard Classification of Education level 0 (ISCED 0).
A Hierarchical Fine-Tuning Based Approach for Multi-label Text Classification

Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.
Ternary Picture as Watermark for Audio Files

Watermark inserted in an audio file may be damaged after attacking this file. The goal of the paper is the development of a class of watermarks which can be recognized by human being even if the watermark saved only a part of the original information. A picture is leveraged as a watermark, and effective ternary coding of such a picture is suggested. The insertion of the watermark is based on the modulation of the container. The resistance of the watermark to various attacks is investigated. The original container is used while watermark extracted.
A Real-Time Feature Indexing System on Live Video Streams

Most of the existing video storage systems rely on offline processing to support the feature-based indexing on video streams. The feature-based indexing technique provides an effective way for users to search video content through visual features, such as object categories (e.g., cars and persons). However, due to the reliance on offline processing, video streams along with their captured features cannot be searchable immediately after video streams are recorded. According to our investigation, buffering and storing live video steams are more time-consuming than the YOLO v3 object detector. Such observation motivates us to propose a real-time feature indexing (RTFI) system to enable instantaneous feature-based indexing on live video streams after video streams are captured and processed through object detectors. RTFI achieves its real-time goal via incorporating the novel design of metadata structure and data placement, the capability of modern object detector (i.e., YOLO v3), and the deduplication techniques to avoid storing repetitive video content. Notably, RTFI is the first system design for realizing real-time feature-based indexing on live video streams. RTFI is implemented on a Linux server and can improve the system throughput by upto 10.60x, compared with the base system without the proposed design. In addition, RTFI is able to make the video content searchable within 20 milliseconds for 10 live video streams after the video content is received by the proposed system, excluding the network transfer latency.
Multiple Instance Learning Via Deep Hierarchical Exploration for Histology Image Classification

We present a fast hierarchical method to detect a presence of cancerous tissue in histological images. The image is not examined in detail everywhere but only inside several small regions of interest, called glimpses. The final classification is done by aggregating classification scores from a CNN on leaf glimpses at the highest resolution. Unlike in existing attention-based methods, the glimpses form a tree structure, low resolution glimpses determining the location of several higher resolution glimpses using weighted sampling and a CNN approximation of the expected scores. We show that it is possible to perform the classification with just a small number of glimpses, leading to an important speedup with only a small performance deterioration. Learning is possible using image labels only, as in the multiple instance learning (MIL) setting.
From QBFs to MALL and Back via Focussing

Abstract In this work we investigate how to extract alternating time bounds from ‘focussed’ proof systems. Our main result is the obtention of fragments of $$\mathsf {MALL} {\mathsf {w} }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>MALL</mml:mi> <mml:mi>w</mml:mi> </mml:mrow> </mml:math> ( $$\mathsf {MALL} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>MALL</mml:mi> </mml:math> with weakening) complete for each level of the polynomial hierarchy. In one direction we encode QBF satisfiability and in the other we encode focussed proof search, and we show that the composition of the two encodings preserves quantifier alternation, yielding the required result. By carefully composing with well-known embeddings of $$\mathsf {MALL} {\mathsf {w} }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>MALL</mml:mi> <mml:mi>w</mml:mi> </mml:mrow> </mml:math> into $$\mathsf {MALL} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>MALL</mml:mi> </mml:math> , we obtain a similar delineation of $$\mathsf {MALL} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>MALL</mml:mi> </mml:math> formulas, again carving out fragments complete for each level of the polynomial hierarchy. This refines the well-known results that both $$\mathsf {MALL} {\mathsf {w} }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>MALL</mml:mi> <mml:mi>w</mml:mi> </mml:mrow> </mml:math> and $$\mathsf {MALL} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>MALL</mml:mi> </mml:math> are $$\mathbf {PSPACE}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PSPACE</mml:mi> </mml:math> -complete. A key insight is that we have to refine the usual presentation of focussing to account for deterministic computations in proof search, which correspond to invertible rules that do not branch. This is so that we may more faithfully associate phases of focussed proof search to their alternating time complexity. This presentation seems to uncover further dualities, at the level of proof search, than usual presentations, so could be of proof theoretic interest in its own right.
Improving Presence in Real-time Architectural Visualization

Architectural visualization is based on diverse content that utilizes virtual structures and environments, such as those in games or movies, along with fundamental values conveyed through visual design communication. This requires an extremely high level of presence owing to characteristics of information delivered via independent real-time configured tangible media. This study aims to improve presence in real-time architectural visualization content and proposes detailed production-process guidelines. To this end, factors necessary for presence improvement were first analyzed. Findings thereof were subsequently used as a basis for proposing realistic expression principles suitable for a real-time environment.
University-Wide e-Text Adoption and Students’ Use of, Preferences for, and Learning with e-Textbooks

The consistent growth of electronic textbooks (e-texts) within higher education contexts has led cheaper, more accessible resources for students. Despite this continued growth, the introduction of technology such as e-texts does not necessarily lend itself to more effective teaching and learning practices. Student perceptions on their use of e-texts and the impact of these tools on their engagement and learning present one source of evidence for determining the efficacy of inclusive digital content delivery systems. This chapter describes a survey study conducted with undergraduate students at Indiana University, who has been running a successful university-wide e-text program since 2012. The study used a subset of the National Survey of Student Engagement, specifically designed to examine students’ use of, preferences for, and perceived learning with e-texts. Data from 284 students indicated that they generally used e-texts in relation to their class-assigned reading activities. Interactive features within the e-text were moderately to infrequently used in relation to their learning practices. Students also indicated that their use of e-texts had generally positive benefits on their learning. From these results, we discuss the implications of further integrating e-texts within higher education through extended support and scaffolding of these tools for both teaching and learning.
Equipment Development Risk Assessment Based on Least Square Support Vector Machine

To realize the scientific management of equipment development, a new method based on Least Square Support Vector Machine (LS-SVM) is proposed for equipment development risk assessment. On the basis of built index system of equipment development risk assessment, LS-SVM is used to establish the risk assessment model for equipment development. The simulation indicates that the method can carry out equipment development risk assessment correctly and effectively.
A Composite Risk Model for Optimizing Information System Security

This chapter describes a two-step decision-support risk model that focuses on investment in information technology security. In the first step, the risk level of each of the system's components is mapped with the goal of identifying the subsystems that pose the highest risk. In the second step, the model determines how much to invest in various technological tools and workplace culture programs to enhance information security. The mode is applied to an information system in an academic institution in Israel. This system comprises 10 subsystems and the three that are responsible to most of the risk are identified. These findings are then used to determine the parameters of the investment allocation problem and find the optimal investment plan. The results of the model's application indicate that monetary incentives and grade cheating are the greatest threats to the system's security. In addition, the results provide support to the claim that information security officials tend to overinvest in security technological tools and underinvest in improving security workplace culture.
Socially-Acceptable Walking Parameters for Wheelchair Automation

Socially-acceptable navigation patterns are key to smoothly integrating robots into human daily life. This paper addresses such issues without attempting to use complex algorithms that are difficult to support with low-level controllers. Through observational data collection, this paper finds average walking patterns that can be adhered to by rough guidelines that provide significant tolerances for low-level robots in varying situations. These guidelines can be applied to many different robotic systems but are intended for use with the WHILL Model C wheelchair, where parameter data has been collected to ensure compatibility. Finally, the concept of rider comfort has been briefly accounted briefly accounted for when adjusting the robot's parameters.
Data Flow Dependent Component Placement of Data Processing Cloud Applications

With the ongoing advances in the area of cloud computing, Internet of Things, Industry 4.0, and the increasing prevalence of cyber-physical systems and devices equipped with sensors, the amount of data generated every second is rising steadily. Thereby, the gathering of data and the creation of added value from this data is getting easier and easier. However, the increasing volume of data stored in the cloud leads to new challenges. Analytics software and scalable platforms are required to evaluate the data distributed all over the internet. But with distributed applications and large data sets to be handled, the network becomes a bottleneck. Therefore, in this work, we present an approach to automatically improve the deployment of such applications regarding the placement of data processing components dependent on the data flow of the application. To show the practical feasibility of our approach, we implemented a prototype based on the open-source ecosystem OpenTOSCA. Moreover, we evaluated our prototype using various scenarios.
A Line Extraction Process Based on Noise Elimination and a Novel Benchmark in Jensen-Shannon Divergence

This paper proposes a line extraction process based on noise elimination and a novel benchmark in Jensen-Shannon divergence. For com-mon noise from sensors, DBSCAN clustering im-proves accuracy and time efficiency in line extraction. Extracted line segments are further adjusted according to our new benchmark based on JS divergence, which is also used to finally evaluate the results against the ground truth. As for the crucial line extraction algorithm, which is a replaceable part, we optimize Split-and-Merge algorithm to split by sum of variance or weight. In comparison with four classic algorithms, three steps in our new pipeline contribute to producing better outcomes in different dimensions.
Sequential Markov Games With Ordered Agents: A Bellman-Like Approach

Markov games, as a framework for multi-agent reinforcement learning, has been well investigated in the past decades. In Markov games, all agents simultaneously select actions at each state. However many situations in the real world have to be modeled as a sequential process with ordered agents, which motivates us to widen the view and concepts of Markov games so that all agents can sequentially select actions in some order. This letter studies a step in this direction where exactly two agents with general objectives select actions sequentially in an infinite horizon process. Based on the framework of Markov games, the Bellman-like operators are proposed to analyze the evolutionary process and dynamic programming algorithms are developed to derive the optimal equilibrium for the proposed sequential model. Finally, the effectiveness of the theoretical results is illustrated via numerical simulations.
Improving task scheduling by using a fuzzy reasoner

In this paper a rule-based fuzzy scheduling method is presented. Aim of this method is to handle the uncertainty and vagueness that characterize the complicated process of task scheduling. It uses fuzzy sets to describe both a task’s deadline and a task’s remaining execution time. The operation of the presented scheduling method is based on a rule-based reasoner that decides dynamically about the priority of the tasks that wait to be executed. This reasoner is triggered each time a change occurs (e.g. the execution of a task ends, a new task arrives etc.). It has been compared with Earlied Deadline First (EDF) scheduling algorithm. The results showed that the presented fuzzy scheduling method improves the scheduling process, since it minimizes both, the mean waiting time and the mean turnaround time.
Energy and Performance Comparison of Cryptocurrency Mining for Embedded Devices

Blockchains are composed of a network of computing entities which maintain an immutable distributed ledger thus facilitating auditability and accountability of transactions. The process of Cryptocurrency mining involves miners verifying transactions and adding them to the ledger. While Cryp-tocurrency mining prevents random nodes from creating and appending blocks, it incurs a negative impact on the embedded devices due to their resource-constrained nature. Thus, a need for profiling the power consumption of cryptocurrency mining platforms for embedded devices becomes necessary. In this work, we comparatively study the performance and power consumption of a suite of commonly used cryptocurrency mining platforms and analyze energy-performance trade-offs. In particular, our analysis is based on the power consumption during mining, changes in the power behavior and their impact on the overall system. Experiments conducted using real devices indicate that mining incurs a 2-fold increase in power consumption compared to those without mining. Our insights can be used to further investigate the impact of cryptocurrency malware on power consumption as well as design algorithms for energy efficient mining.
Local Path Planning for Autonomous Vehicle Based on Artificial Potential Field Algorithm

Applying the artificial potential field algorithm to the intelligent vehicle path planning, it is proposed to use the minimum safe distance as the symmetry according to the sinusoidal obstacle avoidance model. The method of constructing the obstacle point model ensures that the local target point is on the axis of symmetry, and the steering angle is made by the intelligent vehicle traveling speed and the turning radius. Finally, we verified the feasibility and accuracy of the algorithm through the joint simulation of Prescan and Matlab software. The simulation results show that the algorithm can plan a smooth target path and realize the smooth and safe path planning and tracking control of intelligent vehicles.
A teaching module for blackbody radiation using the continuous spectra of stars

Abstract Teaching blackbody radiation within the framework of astrophysics can motivate students to learn and obtain a more profound and qualitative understanding of the subject. In this paper we present a teaching module that intertwines teaching blackbody radiation via the continuous spectrum of hot objects, especially the stars. The module’s approach was that of guided inquiry. Students of upper secondary education carried out experimental activities that included a combination of hands-on activities and computer processing. They actively and creatively engaged in the activities which helped them develop a more complex and structured knowledge of the subject. A ten-multiple choice questionnaire was administered before and after the implementation and the data were qualitatively and quantitatively analyzed. The results showed that the intervention had a significant impact on students’ learning outcomes.
Chapter 1. A comparative study of compliment responses among Chinese Renren users and American Facebook users

Advancements in technology, spread of English language worldwide, increasing use of online social networking sites (SNSs) and globalization are recognized as highly powerful forces influencing everyday language use and practices. This has led to concerns that technologization, in line with globalization, and increasing dominance of English language will lead to the homogenization of the world, reducing cultural and linguistic diversity in discourse practices. which fall within the heart of this interconnectedness (Sifianou 2013). In this paper, we examine if, and to what extent, the use of SNSs lead to homogenization among discourse practices of Chinese and American English users in two similar SNSs (Facebook and Renren [the Facebook of China]), as reflected in their compliment response (CR) strategies. The findings may provide evidence as to the kinds of changes that increased interconnectedness may produce in language use of different speech communities. An analysis of the data revealed that increasing use of online SNSs and interconnectedness does not necessarily mean eradication of cultural differences in discourse practices but does decrease cultural differences in language use. The changes are uni-directional and dominated by the English language and American/Western Cultural values.
A Modular Framework to Facilitate the Control of an Assistive Robotic Arm Using Visual Servoing and Proximity Sensing

This paper presents the implementation of a modular framework that allows the integration of different peripheral components towards facilitating the control of an assistive robotic arm. For that purpose, several components, such as proximity sensors, an RGB camera and a joystick, were integrated into the same control paradigm of the robotic arm. Firstly, the proximity sensors are arranged on the robot casing to sense the nearby workspace. This configuration is helpful to detect obstacles in robot trajectories and ensure the user's safety. Secondly, an RGB camera was fixed in the robot end-effector to capture visible information in order to assist the object's grasping via a visual servoing (VS) strategy. Finally, a multimodal approach was considered to increase the input options for moving the robot by off-the-shelf joysticks. All of the above components were integrated into the control strategy through a software-based modular architecture. The proposed framework aims to provide an intuitive, reliable, and modular solution to facilitate the control of a robotic manipulator in task performing, and extendable to people with disabilities.