Reviewerly/exHarmony huggingface dataset:
MicroFaaS: Energy-efficient Serverless on Bare-metal Single-board Computers

Serverless function-as-a-service (FaaS) platforms offer a radically-new paradigm for cloud software development, yet the hardware infrastructure underlying these platforms is based on a decades-old design pattern. The rise of FaaS presents an opportunity to reimagine cloud infrastructure to be more energy-efficient, cost-effective, reliable, and secure. In this paper, we show how replacing handfuls of x86-based rack servers with hundreds of ARM-based single-board computers could lead to a virtualization-free, energy-proportional cloud that achieves this vision. We call our systematically-designed implementation MicroFaaS, and we conduct a thorough evaluation and cost analysis comparing MicroFaaS to a throughput-matched FaaS platform implemented in the style of conventional virtualization-based cloud systems. Our results show a 5.6x increase in energy efficiency and 34.2% decrease in total cost of ownership compared to our baseline.
The effects of partial proxy embodiment on the awareness of linear distance in omnidirectional treadmill enabled immersive virtual environment

The immersive virtual environment is a very powerful tool, but its applications are limited if the cognitive performance of the users cannot replicate the results achieved in the real environment. This empirical investigation assessed the awareness of linear dimension in the virtual environment through a commercial omnidirectional treadmill and head-mounted display (HMD). Half of the study participants were provided a first-person perspective using the HMD accompanying handsets visible in the virtual environment as proxy embodiment. Their competence was compared against the other half of the participants who were offered a third-person perspective, a non-embodied experience. Evaluations regarding exocentric verbal distance estimation and egocentric physical walk of an intended distance, for the two perceptual perspectives, were gauged against the performance in the real environment. The proficiency of verbal distance estimation between the virtual and the real environments was within the margin of error for the first-person perspective, but the results show that a considerable difference exists in the ability to walk linear distance. Nevertheless, the use of proxy embodiment can significantly reduce the egocentric walk disparity as compared to the non-embodied experience. These findings are expected to further the immersion efforts for precision directed applications such as construction safety training.
Feasibility of Longitudinal Eye-Gaze Tracking in the Workplace

Eye movements provide a window into cognitive processes, but much of the research harnessing this data has been confined to the laboratory. We address whether eye gaze can be passively, reliably, and privately recorded in real-world environments across extended timeframes using commercial-off-the-shelf (COTS) sensors. We recorded eye gaze data from a COTS tracker embedded in participants (N=20) work environments at pseudorandom intervals across a two-week period. We found that valid samples were recorded approximately 30% of the time despite calibrating the eye tracker only once and without placing any other restrictions on participants. The number of valid samples decreased over days with the degree of decrease dependent on contextual variables (i.e., frequency of video conferencing) and individual difference attributes (e.g., sleep quality and multitasking ability). Participants reported that sensors did not change or impact their work. Our findings suggest the potential for the collection of eye-gaze in authentic environments.
Q-Learning for Shift-Reduce Parsing in Indonesian Tree-LSTM-Based Text Generation

Tree-LSTM algorithm accommodates tree structure processing to extract information outside the linear sequence pattern. The use of Tree-LSTM in text generation problems requires the help of an external parser at each generation iteration. Developing a good parser demands the representation of complex features and relies heavily on the grammar of the corpus. The limited corpus results in an insufficient number of vocabs for a grammar-based parser, making it less natural to link the text generation process. This research aims to solve the problem of limited corpus by proposing the use of a Reinforcement Learning algorithm in the formation of constituency trees, which link the sentence generation process given a seed phrase as the input in the Tree-LSTM model. The tree production process is modeled as a Markov’s decision process, where a set of states consists of word embedding vectors, and a set of actions of {Shift, Reduce}. The Deep Q-Network model as an approximator of the Q-Learning algorithm is trained to obtain optimal weights in representing the Q-value function. The test results on perplexity-based evaluation show that the proposed Tree-LSTM and Q-Learning combination model achieves values 9.60 and 4.60 for two kinds of corpus with 205 and 1,000 sentences, respectively, better than the Shift-All model. Human evaluation of Friedman test and posthoc analysis showed that all five respondents tended to give the same assessment for the combination model of Tree-LSTM and Q-Learning, which on average outperforms two other nongrammar models, i.e., Shift-All and Reduce-All.
Construction of OBE Concept Autonomous Learning Mode in University Teaching Based on the Internet

In order to meet the needs of life-long learning of the Internet of Things and the economic and social development of digital learning countries, we must actively explore the teaching mode of digital self-learning courses for college students. This article aims to explore the construction of an autonomous learning model based on the OBE concept of university teaching based on the Internet of Things technology. Through self-study of college students in the environment of Internet of Things and information technology, as well as the micro-course self-directed learning that carries the OBE concept, the results show that most students hold a positive attitude towards the autonomous learning model in the information environment. A survey that carries the OBE concept shows that the scoring dimensions of the content of the students' autonomous learning attitude survey before class are both 4.11. It can be considered that the micro-curriculum resources that carry the OBE concept can effectively help students to conduct autonomous learning.
HyBP: Hybrid Isolation-Randomization Secure Branch Predictor

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different processes, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Physical or logical isolation mechanisms such as using dedicated tables and flushing during context-switch can provide security but incur non-trivial costs in space and/or execution time. Randomization mechanisms incurs the performance cost in a different way: those with higher securities add latency to the critical path of the pipeline, while the simpler alternatives leave vulnerabilities to more sophisticated attacks.This paper proposes HyBP, a practical hybrid protection and effective mechanism for building secure branch predictors. The design applies the physical isolation and randomization in the right component to achieve the best of both worlds. We propose to protect the smaller tables with physically isolation based on (thread, privilege) combination; and protect the large tables with randomization. Surprisingly, the physical isolation also significantly enhances the security of the last-level tables by naturally filtering out accesses, reducing the information flow to these bigger tables. As a result, key changes can happen less frequently and be performed conveniently at context switches. Moreover, we propose a latency hiding design for a strong cipher by precomputing the "code book" with a validated, cryptographically strong cipher. Overall, our design incurs a performance penalty of 0.5% compared to 5.1% of physical isolation under the default context switching interval in Linux.
Effect of electrode and electric current on peat water treatment with continuous electrocoagulation process

Peat water is surface water that inundates an area, formed from a pile of organic material for a long time. Peat water can skin irritation and metabolic disorders. The purpose of this study was to determine the application of the electrocoagulation method to neutralize pH and reduce levels of TSS and Fe in peat water and also to determine the effect of changes in electrode distancing and electric current on decreasing TSS and Fe levels and neutralizing the pH of peat water. The parameters tested include pH, TSS (Total Suspended Solid), and Fe content. The electrocoagulation process used electric current which flows in the direction through the electrodes. Electrocoagulation pipe was installed with electric cable that connected to the power supply with variation of current (1.0; 1.4; and 1. 8 A) and electrode distancing variations (0.5; 0.75; 1 in.). The results of this study show the optimum conditions obtained at 1.8 Ampere and 0.75-inch electrode distance with an increase in pH from 3.5 to 6.8, removing TSS from 128 to 36 mg / L, a decreasing Fe of 78.9% from 2.405 mg/L to 0.506 mg/L.
Study on the Optimization of Macroeconomics Teaching Model Based on Cluster Analysis in the Context of Data

Macroeconomics is a basic course among majors related to economic management. It plays a connecting role and can help students grasp economic phenomena and solve economic problems from an overall perspective. Many teachers still teach macroeconomics using the old teaching method, which ignores students' accepting abilities and preferences. It can not properly encourage pupils to take initiative in their learning, resulting in disconnect between curricular instruction and actual requirements. Teachers should correctly face the teaching reform situation, actively rely on advanced big data technology, upgrade macroeconomics teaching mode, and enhance students' ability to actively learn and solve practical problems and use teaching reform to obtain ideal teaching results. We present an optimization approach for macroeconomics teaching mode based on cluster analysis in the context of data based on this. The model's viability is confirmed through the creation of instructional situations and practical teaching. The experimental results show that students can improve their core knowledge, communication, and learning ability. The method has certain feasibility and effectiveness, which lays a foundation for the optimization of macroeconomics teaching mode.
EM SCA &amp; FI Self-Awareness and Resilience with Single On-chip Loop &amp; ML Classifiers

Securing ICs are becoming increasingly challenging with rapid improvements in electromagnetic (EM) side-channel analysis (SCA) and fault injection (FI) attacks. In this work, we develop a pro-active approach to detect and counter these attacks by embedding a single on-chip integrated loop around a crypto core (AES-256), designed and fabricated using TSMC 65nm process. The measured results demonstrate that the proposed system 1) provides EM-Self-awareness by acting as an on-chip H-field sensor, detecting voltage/clock glitching fault-attacks; 2) senses an approaching EM probe to detect any incoming threat; and 3) can be used to induce EM noise to increase resilience against EM attacks. This work combines EM analysis, ML based secured system and shows the efficacy by measurements from custom-built 65nm CMOS IC.
Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students' facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student's academic mood, they can intervene on the students' positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students' positive academic emotions, in order to improve students' positive academic emotions, which will improve students' academic performance and teaching.
Investigating Contextual Influence in Document-Level Translation

Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the MT model to generate a better translation. In recent times, MT researchers have turned their focus to this line of MT research. As an example, hierarchical attention network (HAN) models use document-level context for translation prediction. In this work, we studied translations produced by the HAN-based MT systems. We examined how contextual information improves translation in document-level NMT. More specifically, we investigated why context-aware models such as HAN perform better than vanilla baseline NMT systems that do not take context into account. We considered Hindi-to-English, Spanish-to-English and Chinese-to-English for our investigation. We experimented with the formation of conditional context (i.e., neighbouring sentences) of the source sentences to be translated in HAN to predict their target translations. Interestingly, we observed that the quality of the target translations of specific source sentences highly relates to the context in which the source sentences appear. Based on their sensitivity to context, we classify our test set sentences into three categories, i.e., context-sensitive, context-insensitive and normal. We believe that this categorization may change the way in which context is utilized in document-level translation.
Application of Radar Solutions for the Purpose of Bird Tracking Systems Based on Video Observation

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds' activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study.
Distributed Dynamic Predictive Control for Multi-AUV Target Searching and Hunting in Unknown Environments

The research and development of the ocean has been gaining in popularity in recent years, and the problem of target searching and hunting in the unknown marine environment has been a pressing problem. To solve this problem, a distributed dynamic predictive control (DDPC) algorithm based on the idea of predictive control is proposed. The task-environment region information and the input of the AUV state update are obtained by predicting the state of multi-AUV systems and making online task optimization decisions and then locking the search area for the following moment. Once a moving target is found in the search process, the AUV conducts a distributed hunt based on the theory of potential points, which solves the problem of the reasonable distribution of potential points during the hunting process and realizes the formation of hunting rapidly. Compared with other methods, the simulation results show that the algorithm exhibits high efficiency and adaptability.
Shape and textural based image retrieval using K-NN classifier

Due to the evolution in the digital domain limitless multimedia is generated daily. It creates a necessity of potential and appealing image resuscitation system. In this paper, a shape and texture-based image retrieval system is proposed that estimates the resemblances of each query image with the images stored in the repository in the form of shape and textural facets and retrieves the images within an expected range of resemblance. The proposed approach employs a statistical approach for image retrieval. The proposed approach takes into account discriminative features of the input image for generating the shape and texture descriptors that produce outstanding results for image databases of restricted variety, which merely includes homogeneous patterns, this approach yielded satisfactory results. For texture images it uses the spatial gray level dependency matrix (SGLDM) and proposes an algorithm to compute the the inverse difference moment (IDM) as the optimal image representative feature. It further employs K-Nearest Neighbour (KNN) classifier for the classification and retrieval tasks. The proposed system outperforms the various other ultra-modern content-based image retrieval (CBIR) systems in many respects.
From traffic classes to content: A hierarchical approach for encrypted traffic classification

The vast majority of Internet traffic is now end-to-end encrypted, and while encryption provides user privacy and security, it has made network surveillance an impossible task. Various parties are using this limitation to distribute problematic content such as fake news, copy-righted material, and propaganda videos. Recent advances in machine learning techniques have shown great promise in extracting content fingerprints from encrypted traffic captured at the various points in IP core networks. Nonetheless, content fingerprinting from listening to encrypted wireless traffic remains a challenging task due to the difficulty in distinguishing re-transmissions and multiple flows on the same link. In this paper, we show the potential of fingerprinting internet traffic by passively sniffing WiFi frames in air, without connecting to the WiFi network by leveraging deep learning methods. First, we show the possibility of building a generic traffic classifier using a hierarchical approach that is able to identity most common traffic types in the Internet and reveal fine-granular details such as identifying the exact content of the traffic. Second, we demonstrate the possibility of using Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) to identify streaming traffic, such as video and music, from a closed set, by sniffing WiFi traffic that is encrypted at both Media Access Control (MAC) and Transport layers. Overall, our results demonstrate that we can achieve over 95% accuracy in identifying traffic types such as web, video streaming, and audio streaming as well as identifying the exact content consumed by the user.
Ways of looking: an eye-tracking study of visual literacy expertise

Eye tracking has been utilized for decades to study perceptual processes in a range of fields, and it has proven particularly useful for studying how the viewing behaviours of experts and novices within a field differ from one another. This article reports on a study that uses eye tracking to examine patterns in the ways that visual communication experts and non-experts read journalistic photographs that they might encounter in their daily lives. Expert participants (29) were practitioners with a minimum of 4–5 years of experience in a visual communication field; non-expert participants (29) had no training in visual communication and had 0–1 year of experience. Participants viewed 10 images and answered a series of four questions about each image. Participant eye movements were tracked utilizing a TobiiPro x2-60 eye tracker connected to a 17-inch high-end gaming laptop. The literature suggests that there should be observable differences between the eye movements of experts and non-experts, with expert viewing behaviour being more efficient and effective. However, the differences between experts and non-expert participants in the study were inconsistent and far less extensive than expected. The article discusses possible explanations for these results and suggests directions for future studies.