Reviewerly/exHarmony huggingface dataset:
Algorithm and Architecture Design of FAST-C Image Corner Detection Engine

First-in-first-out (FIFO) line buffers occupy considerable logic gates and consume significant power of the feature from accelerated segment test (FAST) image corner detection engine. In this study, a FAST-Chip (FAST-C) engine is proposed to eliminate these drawbacks. It comprises three core techniques: arch pixel estimation (APE), hardware-efficient line buffer (HLB), and interleaving-based parallel access (IPA). APE can save three line buffers using gradient information for pixel prediction. With APE, the number of line buffers can be saved by 33%. HLB effectively organizes static random access memories (SRAMs) as line buffers to store reference pixels rather than FIFO. It demands less power and fewer logic gates. IPA makes the FAST-C engine have regular data paths and smooth data scheduling, keeping hardware throughput. Experiment results show that the accuracy difference between FAST and FAST-C is as minor as 2.29%. The proposed FAST-C engine is implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- μm CMOS process with a logic gate count of 223k, where the line buffers are included. Its frequency is 613 MHz with a power consumption of 1.18 W. Its maximum throughput can sufficiently support 3840×2.160(4k) @ 60 frames per second (fps). Compared with sophisticated FAST engines, the FAST-C engine presents outstanding energy efficiency (EE) and area efficiency (AE).
Application of Improved Canny Algorithm in Image of Surgical Instruments

In order to improve the inventory efficiency of surgical instruments, a vision system needs to be installed. The edge detection of the image of surgical instruments is an important step to inventory surgical instruments. However, due to the wide variety of surgical instruments and the different sizes, it is necessary to improve the existing edge detection algorithm to satisfy the actual needs. Canny algorithm has been successfully applied in many computer vision systems, but it needs to be set manually when selecting high-low thresholds, it has poor adaptability and relies on human experience, so it cannot be directly applied to edge detection of surgical instruments. In this paper, based on the study of the traditional Canny algorithm, an improved Canny operator edge detection algorithm is proposed which shows the ability to autonomously select the threshold. Experimental results show that the improved Canny algorithm can effectively retain the true edge information, make the edges more continuous, and effectively achieve the edge detection of surgical instruments compared with the traditional Canny algorithm and the multi-scale morphology algorithm.
Evolvable Hardware Architectures on FPGA for Side-Channel Security

This paper proposes the use of Evolvable Hardware (EH) architectures as a countermeasure against power analysis attacks. It is inspired by the work of Sasdrich et al., in which the block cipher PRESENT is protected against power analysis attacks through the use of dynamic logic FPGA reconfiguration. The countermeasure consists of splitting the substitution boxes (S-boxes) into two parts with a register in between; the way the S-boxes are split is random and is altered before each new execution of the block cipher. This makes it very difficult (or even impossible) for an attacker to perform a Differential Power Analysis (DPA) attack by collecting many power traces of the same implementation.
Multi-task Learning for Low-Resource Second Language Acquisition Modeling

Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned, which is a fundamental building block of the personalized learning system. However, as far as we know, almost all existing methods cannot work well in low-resource scenarios due to lacking of training data. Fortunately, there are some latent common patterns among different language-learning tasks, which gives us an opportunity to solve the low-resource SLA modeling problem. Inspired by this idea, we propose a novel SLA modeling method, which learns the latent common patterns among different language-learning datasets by multi-task learning and are further applied to improving the prediction performance in low-resource scenarios. Extensive experiments show that the proposed method performs much better than the state-of-the-art baselines in the low-resource scenario. Meanwhile, it also obtains improvement slightly in the non-low-resource scenario.
Moods in Classical Paintings: An AI based Classification Approach

Abstract According to WHO, globally there are at least 2.2 billion people who have a vision impairment or blindness. While they are known as the disabled in our society, rarely have they benefited from the development of technology and introduction of new products. That is how I developed this idea of constructing a medium that would transfer visual information to audio forms, which would help this disadvantaged group gain access to “visual experience”. In order to achieve this goal, several essential problems should be solved: how can one establish a technological formula to convert visual in-put to audio output? Extracting visual features from paintings, I could set up my own data collection based on some core features, which can capture the mood of a painting. I then use Convolutional Neural Network to train the AI to link visual and audio in-formation. Loss functions are introduced to minimize the error of the product. After I obtain the mood of my pictures, I will use it to formulate a definitive sound, in the process of which the “mood” gets preserved, such as joy, sorrow, or any artistic theme that is shared both in visual and audio experience. Towards the end of this paper, several experiments will be introduced to test the efficacy of my project. The first part is on the functional efficacy of my AI system. The second part is about my product hypothesis: I will give to two distinct participants one pair of visual input and audial output. Then I will check whether they can recognize the exact theme inherent in the corresponding media. Their successful identifying the same “mood” testifies the success of the project goal of transferring visual experience to audio.
Interactive Visualization for OSGi-based Projects

Big software projects often use architectural frameworks for a consistent structure. OSGi is such a framework to create modular Java applications. The architecture of individual projects, however, is often hidden in configuration files. We propose to visualize projects in a modular framework such as OSGi with an approach to allow users to comprehend the connections within a system. We assist this comprehension using filtering and automatically generated, interactive views of the project. We extend the notion of interactive views with a concept to reproduce configured views for arbitrary system revisions to enhance up-to-date documentation. We have implemented this proposal in the publicly available KIELER project, and have validated it with a large software project in the railway domain.
RayGraphy: Aerial Volumetric Graphics Rendered Using Lasers in Fog

We present RayGraphy display technology that renders volumetric graphics by superimposing the trajectories of lights in indoor space filled with fog. Since the traditional FogScreen approach requires the shaping of a thin layer of fog, it can only show two-dimensional images in a narrow range that is close to the fog-emitting nozzle. Although a method that renders volumetric graphics with plasma generated using high-power laser was also proposed, its operation in a public space is considered quite dangerous. The proposed system mainly comprises dozens of laser projectors circularly arranged in a fog-filled space, and renders volumetric graphics in a fog by superimposing weak laser beams from the projectors. Compared to the conventional methods, this system employing weak laser beams and the non-shaped innocuous fog is more scalable and safer. We aim to construct a new spatial augmented reality platform where computer-generated images can be drawn directly in the real world. We implement a prototype that consists of 32 laser projectors and a fog machine. Moreover, we evaluate and discuss the system performance and characteristics in experiments.
Towards Robust Power Grid Attack Protection using LightGBM with Concept Drift Detection and Retraining

In existing literature, various machine learning models have been applied to detect cyber attacks on the power grid. None of them, however, consider the degradation of the model over time due to the distributed and dynamic nature of the power system. At the same time, they also fail to recognize natural events, such as line maintenance, since they are based on binary classification (attack/no attack). In an effort to develop a cyber security protection strategy that will work robustly for an extended period of time, we develop a methodology based on the LightGBM framework, which performs well for a) Training for multi-class events (no attack/natural event/attack), and b) Fast, dynamic retraining with concept drift detection. We use an ensemble learning-based classifier for classifying the events generated through our Real Time Digital Simulatorwith commercial devices in a Hardware-in-The-Loop setup. The proposed novel classification model outperforms binary classifier-based approaches, resulting in an over 97% effectiveness with the inclusion of concept drift detection and retraining.
Frequency, acceptability, and selection: A case study of clause-embedding

We investigate the relationship between the frequency with which verbs are found in particular subcategorization frames and the acceptability of those verbs in those frames, focusing in particular on subordinate clause-taking verbs, such as think, want, and tell. We show that verbs’ subcategorization frame frequency distributions are poor predictors of their acceptability in those frames—explaining, at best, less than ⅓ of the total information about acceptability across the lexicon—and, further, that common matrix factorization techniques used to model the acquisition of verbs’ acceptability in subcategorization frames fare only marginally better.
A Privacy-Preserving Solution for Proximity Tracing Avoiding Identifier Exchanging

Digital contact tracing is one of the actions useful, in combination with other measures, to manage an epidemic diffusion of an infectious disease in an after-lock-down phase. This is a very timely issue, due to the pandemic of COVID19 we are unfortunately living. Apps for contact tracing aim to detect proximity of users and to evaluate the related risk in terms of possible contagious. Existing approaches leverage BLE or GPS, or their combination, even though the prevailing approach is BLE-based and relies on a decentralized model requiring the mutual exchange of ephemeral identifiers among users' smartphones. Unfortunately, a number of security and privacy concerns exist in this kind of solutions, mainly due to the exchange of identifiers, while GPS-based solutions (inherently centralized) may suffer from threats concerning massive surveillance. In this paper, we propose a solution leveraging GPS to detect proximity, and BLE only to improve accuracy, with no exchange of identifiers. Unlike related existing solutions, no complex cryptographic mechanism is adopted, while ensuring that the server does not learn anything about locations of users.
A multi-objective optimal scheduling strategy for cloud service based on fluctuation cost

With the development of cloud computing technology, cloud services put forward higher requirements for Internet bandwidth, traffic, execution time, cost, etc. At the same time, more and more fierce market competition has led to uncontrollable online business, which has resulted in more and more network fluctuations and business fluctuations. These fluctuation risks make cloud services unstable and beyond expectations. Traditional cloud service scheduling algorithms are more based on time, resource balance, execution cost, etc., which are more suitable for stable environment. When there are fluctuations, these algorithms can not take the impact of fluctuations into account, resulting in unreasonable scheduling strategy. In this study, we propose a multi-objective optimization scheduling strategy for cloud service based on fluctuation cost. We use the fluctuation cost to evaluate the potential impact of the fluctuation, construct the resource sequence based on the fluctuation factor, and obtain the fluctuation cost value through the fluctuation cost algorithm. Combined with execution time optimization, task priority and other objectives, particle swarm optimization scheduling algorithm is improved to meet the multi-objective strategy requirements. Finally, the experiment proves that this strategy can better handle the resource scheduling problem when fluctuations occur. By setting different weight values, it can provide more solutions for user decision-making in the actual situation.
The Role and Trend of Information and Communications Technology Towards a Pervasive Healthcare System

Technological innovations have played a significant role in the modern healthcare system. However, with increasing rate of human population relative to the available medical personnel and facilities, a sudden outbreak of epidemic diseases and its resultant spread into a pandemic may pose a risk to the wellbeing of human populace. Thus, state-of-the-art innovations in communication engineering and technology is able to provide a support mechanism to healthcare domain. This paper analyzed pillars and paradigms in information and communications technology (ICT) deployable in the healthcare domain and estimates a compliance index based on the subset in which the ICT pillars and paradigms are decomposed. The analysis revealed that technological advances in the areas of robotics and internet of things (IoT) paradigm presented a potential support mechanism for healthcare domain. Finally, the paper concluded with insight into the implementation of these technologies and other supporting pillars and paradigms towards a pervasive healthcare system, enlisted recommendations, and future directions.
How to Tune Parameters in Geographical Ontologies Embedding

Many Natural Language Processing (NLP) tasks, like question answering or analyzing verbatim comments, have started to use word embeddings due to their ability to capture semantic relations between words. Recently, embeddings have been also applied in the geospatial context to represent geospatial ontologies, thanks to their ability to capture semantic similarity. In this paper, we present an analysis of a promising embedding technique particularly suitable for representing hierarchical structures. We conduct a deep technical evaluation of many parameters and their impact on the quality of the representation.
The Influence of System Factors on QoE for WebRTC Video Communication

Web Real-Time Communication (WebRTC) is a relatively new technology which enables peer-to-peer communication between web browsers in real time. In order to survive in a competitive market, it is important to satisfy customer expectations, provide expected Quality of Service (QoS) and certain levels of Quality of Experience (QoE). This paper examines the influence of three system factors, i.e., delay, jitter, and Central Processing Unit (CPU), on the QoE of WebRTC video communication. The QoE metrics were collected by questionnaire and statistically analyzed using Friedman’s test and Pearson correlation. The statistical analysis has shown a statistically significant impact of considered system influence factors on QoE.
The bootstrap procedure in classification problems

In classification problems, cross-validation chooses random samples from the dataset in order to improve the ability of the model to classify properly new observations in the respective class. Research articles from various fields show that when applied to regression problems, the bootstrap can improve either the prediction ability of the model or the ability for feature selection. The purpose of our research is to show that the bootstrap as a model selection procedure in classification problems can outperform cross-validation. We compare the performance measures of cross-validation and the bootstrap on a set of classification problems and analyse their practical advantages and disadvantages. We show that the bootstrap procedure can accelerate execution time compared to the cross-validation procedure while preserving the accuracy of the classification model. This advantage of the bootstrap is particularly important in big datasets as the time needed for fitting the model can be reduced without decreasing the model's performance.
Evolution of Neural Text Generation: Comparative Analysis

In the past few years, various advancements have been made in Language Models owing to the formulation of various new algorithms such as Generative Adversarial Networks (GANs), ELMo and Bidirectional Encoder Representations from Transformers (BERT). Text Generation, one of the most important language modeling problems has shown great promise recently due to the advancement of more efficient and competent context-dependent algorithms such as ElMo and BERT and GPT-2 compared to preceding context independent algorithms such as word2vec and GloVe. In this paper, we compare the various attempts to Text Generation showcasing the benefits of each in their own unique form.
Audiouth: Multi-factor Authentication Based on Audio Signal

Despite its popularity, password-based authentication has several crucial security problems. The multi-factor authentication system has risen as a popular alternative method to improve password security by applying additional authentication factors instead of only password. One of the notable examples is a push-based two-factor authentication (2FA) system that requires the user to confirm their login attempt with a very minimum effort. However, this method also has a disadvantage as users can accidentally press the accept button in their software token. In this work, we propose Audiouth as an additional authentication factor to the push-based 2FA based on physical proximity between the login device and smartphone as the token device. Audiouth using an audio signal to measure the proximity so that it does not require additional user interaction. It is also easily deployed because no extra devices needed and it is compatible with current phones, computers, and browsers. The experiment shows that Audiouth is robust in silent and noisy environments, especially in indoor. Besides, Audiouth can also be used to minimize the co-located attack.
Analysis on the Situation of Internet Public Opinion Research

Network public opinion analysis is of great significance to grasp the development trend of hot events and provide policy guidance. Therefore, systematically sorting out the evolutionary trends and research hotspots of research in this field can provide references and references for conducting network public opinion analysis as well as provide guidance for public opinion monitoring of regulatory authorities. To this end, 677 articles firstly selected in the field of network public opinion analysis that published in from 2014 to 2020 from the core database of Web of Science (WOS). Then Citespace was used to perform scientific measurement and visual analysis on them by building and analyzing knowledge graphs from multiple perspectives such as the distribution of research power of countries, institutions, authors and journals, etc. Finally, this paper discovered the research hotspots and evolution trends of the research in this field. Research methods have undergone an evolutionary process from statistics-based, machine learning algorithms to sentiment analysis. Sentiment analysis, social media and opinion discovery have become the hotspots in present research as well as the trend in future research.
SDE based Unified Scheme for Developing Entropy Prediction Models for OSS

Today, so as to meet the user's requirement, modification of software is necessarily required. But at the same time, to incorporate these modifications and requirements there are enormous changes which are made to the coding of the software and over a period of time these changes make the software complex. Largely there are three types of code changes occur in the source code namely, bug repair, feature enhancement &amp; addition of new features, but these changes bring the uncertainty in the bug removal rate. In this paper, these uncertainties have been explicitly modeled and using three-dimensional wiener processes that define the three types of fluctuation; we have come up with an entropy prediction modeling framework with a unified approach. The analytical solution of the equation is interpreted using Itô’s process. The models are fitted on three real life projects namely Avro, Hive and Pig of Apache open source software (OSS) The experimental findings show that present models exhibit accurate estimation results and have strong prediction skills.
Knowledge Graph Attention Network Enhanced Sequential Recommendation

Knowledge graph (KG) has recently been proved effective and attracted a lot of attentions in sequential recommender systems. However, the relations between the attributes of different entities in KG, which could be utilized to improve the performance, remain largely unexploited. In this paper, we propose an end-to-end Knowledge Graph attention network enhanced Sequential Recommendation (KGSR) framework to capture the context-dependency of sequence items and the semantic information of items in KG by explicitly exploiting high-order relations between entities. Specifically, our method first combines the user-item bipartite graph and the KG into a unified graph and encodes all nodes of the unified graph into vector representations with TransR. Then, a graph attention network recursively propagates the information of neighbor nodes to refine the embedding of nodes and distinguishes the importance of neighbors with an attention mechanism. Finally, we apply recurrent neural network to capture the user’s dynamic preferences by encoding user-interactive sequence items that contain rich auxiliary semantic information. Experimental results on two datasets demonstrate that KGSR outperforms the state-of-the-art sequential recommendation methods.
The Cooperative Management of Complex Knowledge in Planning: Building a Semantic-Based Model for Hydrological Issues

The management of issues related to water resources, a highly complex domain, has increasingly highlighted the critical role of knowledge towards shared, useful and effective planning decisions.Hydrology is an applied science with a very large theoretical base, its corpus borders with many others science domains. The clarification of theoretical, methodological, data, language and meaning issues and differences is of central importance. Therefore, the development of a knowledge management system with semantic extensions can meet some of the needs described.The main objective of this work is to investigate the potential for implementing a knowledge management system with semantic extensions, as well as to propose a functional architecture.To achieve that, first a KMS with semantic exstensions has been implemented and then the same system has been populated with an experimental knowledge content.Furthermore, a bottom-up extraction from the KMS of a simple ontology representing the data inserted in the KMS is considered, in order to show the KMS feature of clarifying and improving inter-domain communication, to enhance a common semantic understanding.