Reviewerly/exHarmony huggingface dataset:
aChain: A SQL-Empowered Analytical Blockchain as a Database

In various multi-party cooperations data stored on blockchains (i.e., on-chain data) should be decentralized consistent, verifiable, traceable, and immutable. Online analytical processing (OLAP) services are critical requirements in these applications. However, OLAP performances of existing blockchain systems are much worse than those of relational databases due to the lack of SQL support. In this paper, we propose a novel SQL-empowered analytical blockchain framework, aChain. It fully provides SQL-based OLAP services, while keeping the secure characteristics. Specifically, aChain relationally reorganizes on-chain data to support full SQL executions. Then, a relational versioning scheme is designed to ensure the atomicity and consistency of transactions. Furthermore, SQL-based APIs are designed based on an execute-order-validate architecture. Finally, we demonstrate that the performance of aChain and MySQL (in both cluster and non-cluster models) is at the same level on a typical OLAP benchmark, TPC-H.
PMBM Filter for Multiple Extended Targets With Unknown Clutter Rate and Detection Probability

In a multitarget tracking (MTT) scenario, the prior knowledge of parameters such as clutter rate and detection probability are usually uncertain or estimated offline from training data. However, significant parameters mismatch in the clutter and detection model will result in biased estimates. In such cases, the ability to adaptively online estimate clutter rate and detection probability is critical in MTT under the random finite sets (RFS) framework. In this article, we propose a robust Poisson multi-Bernoulli mixture (PMBM) filter that can accommodate model mismatch in clutter rate and detection probability for multiple extended target tracking (ETT) problems. Moreover, the closed-form solution to the proposed method is derived by the use of Beta and Gamma Gaussian inverse-Wishart (GGIW) distribution, where Beta is used to describe unknown detection probability and GGIW is used to model extended target extension as an ellipse. Simulation results are presented to verify the effectiveness of the proposed method.
Pixel-Wise Gamma Correction Mapping for Low-Light Image Enhancement

Low-light image enhancement aims to improve the visual quality of images captured under poor illumination and has caught much attention these years. However, existing low-light enhancement methods encounter many problems, e.g., they may not be robust to diverse low-light conditions or have to sacrifice computational efficiency for enhancement performance, which hinder their practical applications. To solve these problems, this paper proposes a novel enhancement method, called Pixel-Wise Gamma Correction Mapping (PWGCM), which combines our innovative pixel-wise Gamma Correction (GC) and deep learning. Compared with conventional GC, our pixel-wise GC is characterized by a set of gamma correction maps, which have the same size as the input image and are taken to replace the single global GC parameter of conventional GC. These gamma correction maps are generated from the low-light image input by a lightweight convolutional neural network at low computational cost. New no-reference loss functions are provided to train the network, ensuring reliable unsupervised learning. Furthermore, our PWGCM is enhanced by an iterative strategy, under which the low-light input image is iteratively enhanced based on the generated gamma correction maps and can yield visually pleasant results. Extensive experiments are done to compare our PWGCM with several state-of-the-art methods in terms of visual quality, efficiency, and auxiliary effects on high-level tasks. The comparison results confirm the superiority of our PWGCM.
The Effect Of Using Animation Videos In English Teaching On Students’ Listening Skills: An Experimental Study At Sma N 10 Batanghari Grade X Academic Year 2016/2017

Purpose of the study: The purpose of this study was to determine the significant effect of using animated videos in teaching on students' listening skills.&#x0D; Methodology: This research used a true experimental design with pre and posttest design. The sample was tested by using pretest-posttest control group designs. There were four classes for tenth grade student there. It was started from X1 to X4. The design of this research was an experimental research. The population of this research was the tenth (X) grade students of SMA N 10 Batanghari, in 2016/2017 of academic year. The totals sample is of tenth graders are 113 students. The classes were taken for experimental and control classes were X3 and X4. In experimental research, we need pre-test and post-test to measure the ability of the students. The data gathered from students’ score in posttest and pretest from control and experimental class. The data were analyzed by using the independent t-test to see the significant effect of using animation videos on students’ listening skills. This research compared to both of experimental class and control class by using media in teaching. The experimental class used some animation videos, while the control class used the conventional way. The formula of T-test.&#x0D; Main Findings: The findings of this study indicate that the use of animated videos has a significant effect in teaching English on students' listening skills at SMA N 10 Batanghari.&#x0D; Novelty/Originality of this study: This study uses animated videos in teaching English to determine the effect on students' listening skills.
Spoken Turkish in television news and debates: Some acoustic and morphological aspects relevant to respeaking

This paper presents a qualitative analysis of spoken Turkish based on approximately 77 minutes of transcribed content from television news and debates with special focus on acoustic and morphological aspects relevant to respeaking, where an edited form of the original verbal content is dictated to a speaker-dependent automatic speech recognition (ASR) engine, whose output is further edited for broadcast-quality subtitles. The data suggest that respeaking can solve only some of the potential problems unscripted speech presents for ASR. On the acoustic level, a respeaker can overcome segmental and suprasegmental variation as well as degraded acoustic conditions, and can partially resolve overlapping speech. On the morphological level, disfluency and deviant morphology can be handled. When paraphrasing the text and dictating punctuation marks under time pressure, a respeaker can hardly control her own pronunciation; deletion, reduced morphemes, etc., might lead to misrecognition. As standard orthography, capitalization, and punctuation are required in subtitles, named entities, figures, morphophonemics and use of apostrophe will require satisfactory solutions during ASR and/or post-editing.
Semantics of Turkish bare nouns and the function of classifiers

The issue of what bare nouns denote is particularly relevant in studies of classifier constructions, given that a classifier serves to mediate the relation between a numeral and a noun. This study addresses the semantics of Turkish bare nouns within the context of numeral classifiers and kind terms, and demonstrates, in line with the ambiguity view, that they are globally ambiguous between object and kind readings, and that the role of classifiers and kind terms is to restrict the domain of entities to object and kind individuals respectively. The analysis developed is further shown to account for a wide range of interpretive possibilities associated with bare nouns in Turkish.
Moving ahead with caution

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This paper identified that knowledge management is essential to encourage an open learning environment where the aspects of the adoption of artificial intelligence can be further investigated. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Chapter 6. The final-appendage construction in Japanese and Korean

Japanese and Korean are both predicate-final (OV) languages with relatively flexible constituent order. However, our analysis of parallel texts (Japanese novels and their Korean translations) demonstrates that the two languages differ in the exploitability of post-predicative position. Korean has a much lower frequency of final-appendage structures with clause-internal elements (especially adnominals and subject nominals) less likely to occur in post-predicative position. On the other hand, Korean shows a relatively higher proportion of vocative phrases in post-predicative position despite a lower frequency of final-appendage structures. The results reveal that Japanese exhibits a relatively higher degree of constructional entrenchment of final-appendage structures. In contrast, post-predicative position in Korean is rather restrictive, more limited to clause-external elements than to clause-internal ones.
Artificial Intelligence and Automation

In this chapter, we discuss artificial intelligence (AI) and its impact on automation. We explain what AI is, namely, the study of intelligent agents, and explain a variety of AI techniques related to acquiring knowledge from observations of the world (machine learning), storing it in a structured way (knowledge representation), combining it (reasoning), and using it to determine how to behave to maximize task performance (planning), in both deterministic and probabilistic settings and for both single-agent and multi-agent systems. We discuss how to apply some of these techniques, using automated warehousing as case study, and how to combine them. We also discuss the achievements, current trends, and future of AI as well as its ethical aspects.
Automation and Ethics

This chapter surveys aspects of automation in technology, its design, implementation, and usage, as they interact with values that underpin our society and its civilization. The framework chosen for this survey is that of moral or ethical theories and attitudes. To this avail we describe several of the ethical theories used currently to anchor discussions about values in our society. Several significant failures of automatic systems, including a fictional one, are quarried for ethical insights and lessons. The currently intractable nature of “neural networks” artificial intelligence systems trained via machine learning is shown to be a moral conundrum. The concepts of code of ethics and of ethical analysis are presented in some detail.
Boosting DNN Cold Inference on Edge Devices

DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use cases, it's not likely to pack all DNNs into device memory and expect that each inference has been warmed up. Therefore, cold inference, the process to read, initialize, and execute a DNN model, is becoming commonplace and its performance is urgently demanded to be optimized. To this end, we present NNV12, the first on-device inference engine optimizing cold inference. NNV12 is built atop three novel optimization knobs: selecting a proper kernel (i.e., operator implementation) for each DNN operator, bypassing the weights transformation process by caching the post-transformed weights on disk, and pipelined execution of many kernels on asymmetric processors. To tackle with the huge search space, NNV12 employs a heuristic-based scheme to obtain a near-optimal kernel scheduling plan. We fully implement a prototype of NNV12 and evaluate its performance across extensive experiments. It shows that NNV12 achieves up to 15.2× speedup compared to the state-of-the-art DNN engines on edge CPUs and 401.5× speedup on edge GPUs, respectively.
Poster: Rethinking Embedded Sensor Data Processing and Analysis with Large Language Models

An important step in the deployment of wireless embedded systems is the analysis of the sensor data. Traditionally, this requires machine learning models tailored to the application use case. However, this step requires significant expertise from the end user and can be less adaptable to the dynamics of real-world deployments. In recent years, large language models have seen significant developments. These models have been shown to be capable of performing general-purpose tasks. In this work, we explore the hypothesis that large language models can be used to aid in sensor data analysis. Our preliminary findings through real-world experiments show significant promise for two tasks: inferring hand gestures through tracking of light and vibration sensor data. We believe these findings highlight the potential of large language models in sensor data analysis, and thus, it warrants further study.
Demo: 3TierView - A Three-tier Privacy-preserving Live Video Surveillance IoT System

This demo presents a working prototype of a live video surveillance IoT system that provides multi-class privacy preservation. The system can use a Raspberry Pi or a laptop to perform real-time face detection, compression and encryption on live video feeds. We design and implement novel methods to accelerate face detection and reduce miss detection for fast moving subjects. The implemented prototype demonstrates the feasibility to provide multi-class privacy-preserving feature and fine-grained access control of video content for real-time IoT applications.
EEFL: High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices

Energy efficiency is essential for federated learning (FL) over mobile devices and its potential prosperous applications. Different from existing communication efficient FL research efforts, which regard communication energy consumption as the bottleneck, we have observed that with ever increasing wireless transmission speed (e.g., Wi-Fi 5 or 5G), the energy consumption of wireless communications for model updates in FL is significantly reduced and sometimes is smaller than that of local on-device training. Motivated by such observations, in this paper, we propose a high-speed wireless communications inspired energy efficient federated learning over mobile devices (EEFL), whose goal is to reduce the overall energy consumption (computing + communication). In particular, we design a novel energy-aware adaptive local update policy for mobile devices by jointly considering FL performance and energy saving of high-speed wireless transmissions. Furthermore, given the device's local update policy in each FL global round, we advance the dynamic voltage and frequency scaling (DVFS) strategy to minimize local training's energy consumption by keeping GPU and CPU working at appropriate frequencies without triggering thermal throttling. Extensive experimental results with various learning models, datasets, and wireless transmission environments demonstrate the proposed EEFL's superiority over the peer designs in terms of energy efficiency.
Uncertainty Propagation of Missing Data Signals with the Interval Discrete Fourier Transform

The interval discrete Fourier transform (DFT) algorithm can propagate signals carrying interval uncertainty.By addressing the repeated variables problem, the interval DFT algorithm provides exact theoretical bounds on the Fourier amplitude and estimates of the power spectral density (PSD) function, whilst running in polynomial time.Thus, the algorithm can be used to assess the worst-case scenario in terms of maximum or minimum power, and to provide insights into the amplitude spectrum bands of the transformed signal.To propagate signals with missing data, an upper and lower value for the missing data present in the signal must be assumed, such that the uncertainty in the spectrum bands can also be interpreted as an indicator of the quality of the reconstructed signal.For missing data reconstruction, there are a number of techniques available that can be used to obtain reliable bounds in the time domain, such as Kriging regressors or interval predictor models.Alternative heuristic strategies based on variable -as opposed to fixed -bounds 1 This is a peer-reviewed, accepted
Demo: Horizon: a Real-time Point Cloud Video Streaming System over Wireless Networks

As a popular way of representing holographic video or volumetric video, point cloud video can provide users with a highly immersive viewing experience of 6 degrees of freedom (6DoF) and is expected to become the mainstream video format of the future. However, the real-time transmission of point cloud video faces many challenges due to the huge amount of data and the large search space of the optimization problem with constraints. To this end, we propose Horizon, a novel Dynamic Adaptive Streaming over HTTP (DASH) based real-time point cloud video streaming system, which aims to maximize the user's viewing experience by predicting the next several steps through a rolling framework and uses a Deep Reinforcement Learning (DRL) based algorithm to achieve a real-time solution to the rolling optimization problem. We have prototyped this system and demonstrated its performance on a state-of-the-art wireless network.
Demo: Landscape: Saliency and Trajectory based Viewport Prediction in Point Cloud Video Streaming

Efficient point cloud video streaming requires accurate viewport prediction, and research on this topic is still in its infancy. This paper demonstrates a high-precision scheme for viewport prediction in the point cloud video, named Landscape, exploring both video saliency information and viewport trajectory. Specifically, we first propose a novel point cloud video sampling method, which reduces computational load while preserving video features. Furthermore, we introduce a new saliency detection technique that integrates temporal and spatial information to detect dynamic, static geometric, and color salient regions. Finally, we intelligently fuse saliency and trajectory information to achieve more accurate viewport prediction. We verify the performance of our proposed viewport prediction methods over state-of-the-art wireless networks.
"Is this my president speaking?" Tamper-proofing Speech in Live Recordings

Malicious editing of audiovisual content has emerged as a popular tool for targeted defamation, spreading disinformation, and triggering political unrest. Public speeches and statements of political leaders, public figures, or celebrities are particularly at target due to their effectiveness in influencing the masses. Ubiquitous audiovisual recording of live speeches with smart devices and unrestricted content sharing and redistributing on social media make it difficult to address this threat using existing authentication techniques. Given public recordings of live events lack source control over the media, standard solutions falter. This paper presents TalkLock, a speech integrity verification system that can enable live speakers to protect their speeches from malicious alterations even when the speech is recorded by any member of the audience. The core idea is to generate meta-information from the speech signal in real-time and disseminate it through a secure QR code-based screen-camera communication. The QR code when recorded along with the speech embeds the meta-information in the content and it can be used later for independent verification in stand-alone applications or online platforms. A user study with live speech and real-world experiments with different types of voices, languages, environments, and distances show that TalkLock can verify fake content with 94.4% accuracy.
Poster: Towards Battery-Free Machine Learning Inference and Model Personalization on MCUs

Machine learning (ML) is moving towards edge devices. However, ML models with high computational demands and energy consumption pose challenges for ML inference in resource-constrained environments, such as the deep sea. To address these challenges, we propose a battery-free ML inference and model personalization pipeline for microcontroller units (MCUs). As an example, we performed fish image recognition in the ocean. We evaluated and compared the accuracy, runtime, power, and energy consumption of the model before and after optimization. The results demonstrate that, our pipeline can achieve 97.78% accuracy with 483.82 KB Flash, 70.32 KB RAM, 118 ms runtime, 4.83 mW power, and 0.57 mJ energy consumption on MCUs, reducing by 64.17%, 12.31%, 52.42%, 63.74%, and 82.67%, compared to the baseline. The results indicate the feasibility of battery-free ML inference on MCUs.
BioCase: Privacy Protection via Acoustic Sensing of Finger Touches on Smartphone Case Mini-Structures

Finger biometrics are widely used by smartphones as a secure and user-friendly credential for privacy protection. However, this information is difficult to measure without high-resolution images, leaving most works to treat this as an image-domain problem. We demonstrate that low-effort alternatives on smartphones are possible through the use of sound propagation in ubiquitous smartphone cases. Inexpensive and widely adopted, smartphone cases are always in contact with fingers, making them ideal for collecting finger biometrics. We thus design BioCase, an acoustic sensing system that leverages smartphone cases equipped with mini-structures to capture unique biometric-hybrid signatures (i.e., reflections influenced by the user's fingertip physiology and behavior) for smartphone privacy protection. The system generates inaudible structure-borne sound and measure the propagation through the smartphone case, mini-structures, and user finger. The design of the mini-structure controls the behavior of structure-borne sound such that unique responses are produced when different users and fingers touch the smartphone case. This enables low-cost, low-effort privacy protection, merely touching the smartphone case can authenticate users. Comprehensive experiments with 46 users over 10 weeks demonstrate BioCase can differentiate users with over 94% accuracy at a 5% false positive rate.
Three lines model paradigm shift: a blockchain-based control framework

Purpose While the three lines model (TLM) provides an organizational structure to execute risk and control duties, research and practice show limitations in the model's implementation. These limitations result in governance issues. Such issues, together with control weaknesses, could be addressed by leveraging properties of distribution, transparency, and immutability of blockchain technology. To this end, in this paper the authors propose a conceptual control framework based on blockchain technology to augment control practice. Design/methodology/approach The design of the resulting blockchain-based control framework (BBCF) and its prototype, based on the design science research methodology (DSRM), is presented and discussed in terms of the potential impact in the context of the identified problems within the TLM. Findings One potential outcome of BBCF could be to redefine the scope and boundaries of some of the activities in audit and control practices from a more static to a more dynamic and prospective role. In a larger context of improving governance practices, the BBCF could set the path for a more inclusive and participatory interaction between the different governance actors of an organization. Research limitations/implications However, this assumes that blockchain is more widely adopted despite its complexity and rigidity. Practical implications BBCF covering both a conceptual model design and a reference implementation provides an innovation in audit and control. BBCF could include all relevant stakeholders who have an interest in corporate governance and control activities, including the regulators. Originality/value The contribution intends to serve both as a starting point for discussing the evolution of audit and control practice based on blockchain technology, as well as an initial actionable prototype for experimentation and further development.