Reviewerly/exHarmony huggingface dataset:
Constructing the Power Knowledge graph by Multi-source Electricity Data

Electricity is an important component of people's lives, and the methods of processing electricity data are also crucial. Currently, electricity data are published on major official and news websites in a distributed and discrete form. Therefore, it is a vital issue to efficiently obtain various types of electricity data for query and utilize. Knowledge graph can provide effective support to solve this point. This paper used the multi-source heterogeneous electricity data, including provincial electricity consumption in 2018 and the statistical data of national industrial electricity consumption in 2018, to construct the corresponding knowledge subgraph, and then merged these two subgraphs to obtain the multi-source power knowledge graph.
Harmonizing Big Data with a Knowledge Graph: OceanGraph KG Uses Case

In this paper we introduce recent efforts carried out by the OceanGraph KG project to integrate semi-structured or unstructured content. We present some of the practical applications of OceanGraph through use cases, and finally summarize the lessons learned during the development process.
Metro Pedestrian Detection Based on Mask R-CNN and Spatial-temporal Feature

In this paper, we introduce the Mask R-CNN, an object detection method based on deep learning networks, to detect the number of pedestrians from surveillance video in the metro train carriage and on the metro station platform, and introduce the fusion of the multi-frame processing result to reduce the detection error. In order to apply and analyze the detection result, we establish a spatial-temporal model of the number of pedestrians in the carriage and on the platform. The experiment shows the efficient result of our method. The average accuracy of the single-frame detection is 73.43%. By fusing the detection result of frames in time series, the average accuracy is 88.85%, which increases 21%. The data of pedestrians' numbers produced by our method can be helpful for metro management, pedestrian guidance, emergency management and so on.
Improving Soft Skills in Agile Software Development by Team Leader Rotation

New agile techniques have brought advantages over traditional techniques. However, agile techniques fail to solve soft skills problems. People with high technical knowledge still find difficulties to adapt to industrial work environments due to the lack of soft skills. At the individual level, specific skills are not perfected, questioned or transmitted. Team members with weaknesses in leadership and communication do not establish a regular channel for dissemination and resolution of disputes. Soft skill problems must be resolved quickly, at least, at the beginning of a project. Thus, people with high potential can lead to having a high impact on development iterations. This paper presents an agile technique taking into account weaknesses in soft skills. The proposed technique is based on rotating the leader team according to an iteration. Experimental evaluation shows that team members reach minimum communication soft skills after a short time. At the same time, teams gain more cohesion and better knowledge of everyone.
AutoADR

Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, these pre-trained models are memory and computation intensive, hindering their deployment into industrial online systems like Ad Relevance. Meanwhile, how to design an effective yet efficient model architecture is another challenging problem in online Ad Relevance. Recently, AutoML shed new lights on architecture design, but how to integrate it with pre-trained language models remains unsettled. In this paper, we propose AutoADR (Automatic model design for AD Relevance) --- a novel end-to-end framework to address this challenge, and share our experience to ship these cutting-edge techniques into online Ad Relevance system at Microsoft Bing. Specifically, AutoADR leverages a one-shot neural architecture search algorithm to find a tailored network architecture for Ad Relevance. The search process is simultaneously guided by knowledge distillation from a large pre-trained teacher model (e.g. BERT), while taking the online serving constraints (e.g. memory and latency) into consideration. We add the model designed by AutoADR as a sub-model into the production Ad Relevance model. This additional sub-model improves the Precision-Recall AUC (PR AUC) on top of the original Ad Relevance model by 2.65X of the normalized shipping bar. More importantly, adding this automatically designed sub-model leads to a statistically significant 4.6% Bad-Ad ratio reduction in online A/B testing. This model has been shipped into Microsoft Bing Ad Relevance Production model.
Sources of High School Student First Amendment Knowledge

Support and engagement with the First Amendment among high school students is at a high level; however, little is known regarding the ways high school students learn about the First Amendment. This study examines what sources students learn about the First Amendment from, and if some sources are considered more valuable than others. This study focuses on three primary possible First Amendment sources: parents, classes, and media. This issue is examined through a survey of high school students. The study indicates that classes are the most frequent, and most valuable, source of First Amendment knowledge.
Evaluating Generalisation in General Video Game Playing

The General Video Game Artificial Intelligence (GVGAI) competition has been running for several years with various tracks. This paper focuses on the challenge of the GVGAI learning track in which 3 games are selected and 2 levels are given for training, while 3 hidden levels are left for evaluation. This setup poses a difficult challenge for current Reinforcement Learning (RL) algorithms, as they typically require much more data. This work investigates 3 versions of the Advantage Actor-Critic (A2C) algorithm trained on a maximum of 2 levels from the available 5 from the GVGAI framework and compares their performance on all levels. The selected sub-set of games have different characteristics, like stochasticity, reward distribution and objectives. We found that stochasticity improves the generalisation, but too much can cause the algorithms to fail to learn the training levels. The quality of the training levels also matters, different sets of training levels can boost generalisation over all levels. In the GVGAI competition agents are scored based on their win rates and then their scores achieved in the games. We found that solely using the rewards provided by the game might not encourage winning.
Effects of Negative Sampling on Knowledge Graph Completion

Knowledge graph embedding concepts have become popular in recent years. The most common usage area of this concept is knowledge graph completion because ordinarily, knowledge graphs consist of a large amount of structured data, but they are not complete. To perform the completion task, embedding models use positive triples, which are in the knowledge graph and some negative triples which artificially generated for each positive triples. This study is aimed to investigate the effects of negative sampling on the knowledge graph completion task. Several experiments are realized with well-known knowledge graph embedding models on different sized knowledge graphs to show these effects. We applied TransE, ComplEx, DistMult, and ConvKB models to Kinship and FB15k-237 knowledge graphs. In addition, as negative sampling method, we used random-corruption negative sampling.
Utilizing <scp>HTML</scp>‐analysis and computer vision on a corpus of website screenshots to investigate design developments on the web

Abstract We present preliminary results of a project investigating the design development of popular websites between 1996 and 2020 via HTML analysis and basic computer vision methods. We acquired a corpus of website screenshots of the current top 47 popular websites. We crawled a snapshot of every month of these websites via the wayback machine of the Internet Archive platform since the time snapshots are stored to gather 7,953 screenshots and HTML pages. We report upon quantitative analysis results concerning HTML elements, color distributions and visual complexity throughout the years.
How AI Technology is Dramatically Improving Video Compression for Broadcast and OTT Content Delivery

Video compression for broadcast TV services started more than 20 years ago. Since then, there has been a major codec standard created about every 10 years, with MPEG-2 released around 1995, AVC in 2005 and HEVC in 2015. Over time, several key improvements such as dual-pass encoding, statistical multiplexing and software migration were made to compression technology in order to boost performance. Tuning the algorithms and the algorithmic tools also allowed significant improvements to be made. — Now a new and disruptive technology — Artificial Intelligence (AI) — is driving the next frontier of video compression enhancements with the promise of faster advancements. AI is being used to improve several key areas: to achieve better video quality (VQ) at a given bit rate, or lower bit rate at the same VQ. Advances are also being made in other directions, like higher density to where the same VQ/bitrate efficiency will use less computing resources, and in providing better quality of experiences (QoE). — This paper will present three examples of AI applied to video encoding to optimize broadcast and OTT content delivery: Dynamic Encoding Style (DES) for a better VQ /bitrate trade-off, Dynamic Resolution Encoding (DRE) for better QoE and density, and Dynamic Frame rate Encoding (DFE) also for improved density and QoE. — In addition, the paper will explore the operational and end-user benefits enabled by AI and machine learning. Additionally, it will provide measurement for the applications that are presented and address the possible future evolutions of AI for video compression.
Design, Development and Delivery of a Complimentary STEM Program for Primary School Pupils

Presently, in Singapore and most of the countries, many students have avoided doing STEM courses because it is difficult and it does not pay them well after graduation. The country is experiencing lack of good engineers, technologists and scientists [1], [2]. An initiative has been started to introduce STEM to students at young age in a fun and yet effective learning way. Team TBSS designed and developed a 6-hour STEM targeting pupils between 10 and 12 years old who are reading primary 4 and primary 6 in school. The program has been conducted several times with Henry Park Primary School. The process, findings and experiences will be shared in this paper.
Prospects of time-bin quantum key distribution in turbulent free-space channels

Quantum key distribution is a quantum communication protocol which seeks to address potential vulnerabilities in data transmission and storage. One of the main challenges in the field is achieving high rates of secret key in lossy and turbulent free-space channels. In this scenario, most experimental demonstrations have used the polarization of photons as their qubit carriers, due to the relative robustness of polarization in free-space propagation. Time-bin or phase-based protocols are considered less practical due to the wave-front distortion caused by atmospheric turbulence. However, demonstrations of novel free-space interferometer designs are enabling interferometers to measure multimodal signals with high visibility. That means it is now viable to consider the prospects of implementing time-bin or phase-based protocols, which have demonstrated high key rates and long transmission distances in optical fiber. In this work, we present the possibilities of implementing time-bin protocols in turbulent free-space channels, using the coherent one-way protocol as the example. We present an analysis of the secret key rate and quantum bit error rate of the system, providing the errors due to noise counts, and the extinction ratio of the pulses. Finally, we developed a model to quantify the expected losses for a turbulence free-space channel, specifically for a free-space satellite-to-ground station channel.
Analyzing Five Conscious and Unconscious Behaviors Using Google n-gram Database Generated from Millions of Books

Due to the exponential increase in the proliferation of information by humans and machines, the 21st century is called the "Information Age" and the "Digital Age". Regardless of the size of the data at hand, the challenge always is to clear, combine, analyze and transform the data into meaningful information. In this article, the words "eating, hearing, seeing, guiding, humming" in Google n-gram corpus are examined along with their adjacent adjectives indicating to elucidate whether they refer to a conscious action or not. The Google n-gram corpus was generated from millions of scanned books published between 1500 and 2008. Books are the expression of human emotions, thoughts and behaviors. The frequency of the usage of certain words in books can be considered as a marker to estimate the importance of the concept represented by that word. Also, the adjective that is frequently used to modify a noun can provide us with the information about the characteristics of the concept that the noun represents. In this study, an extensive body of data derived from the published books have been used to analyze conscious and unconscious behaviors. Time-dependent conscious and unconscious behavior trends were obtained by using linear regression. The uniqueness of this study lies in the fact that it uses written statements for conscious and unconscious behavior analysis. Among the five behaviors examined, the most common words in the books are "conscious eating". The use of this phrase also showed the highest increase over time. Although "unconscious eating" has almost the same increments as "conscious seeing", the concept of "unconscious seeing" has found little place in the books.
Wayang Kulit Movement Control System Using Kinect Sensor

Wayang kulit is a traditional leather puppet from Indonesia that starts to be less popular compared to the other kinds of entertainment. Due to small number puppeteers, called dalang, regeneration is very important for conserving this traditional art. In order to contribute to this regeneration, we conducted a study aiming to control Wayang's movement based on human hand movement. Kinect sensor was utilized to detect human movement. The movements were recorded and processed to calculate four angles: right shoulder, left shoulder, right elbow, and left elbow using skeleton tracking method. The four angles were then sent to microcontroller to control four servo motors installed in the Wayang. Based on our experiment, error of the Kinect sensor reading and the servo movement were 3.6° and 4.87°, respectively.
Quantifying (Hyper) Parameter Leakage in Machine Learning

Machine Learning models are extensively used for various multimedia applications and are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox models are commercially valuable to adversaries, making them vulnerable to extraction attacks that reverse engineer the proprietary model thereby violating the model privacy and Intellectual Property. Extraction attacks proposed in the literature are empirically evaluated and lack a theoretical framework to measure the information leaked under such attacks. In this work, we propose a novel model-agnostic probabilistic framework, AIRAVATA, to quantify information leakage using partial knowledge and limited evidences from model extraction attacks. This framework captures the fact that extracting the exact target model is difficult due to experimental uncertainty while inferring model hyperparameters and stochastic nature of training for stealing the target model functionality. We use Bayesian Networks to capture uncertainty in estimating the target model under various extraction attacks based on the subjective notion of probability. We validate the proposed framework under different adversary assumptions commonly adopted in the literature to reason about the attack efficacy. This provides a practical tool to identify the best attack combination which maximises the knowledge extracted (or information leaked) from the target model and estimate the relative threats from different attacks.
Thematic spectral library for remote sensing monitoring of land covers in local scale

Monitoring of the Earth land covers is one of the most important areas in applying remote sensing data. In-situ measurements are necessary to confirm the performance of remote sensing devices through evaluation of data quality. These measurements are performed in established space test sites and serve as datasets presented in open database for referencing remotely sensed data to ground-truth spectral ones, for enhancement of data accuracy and for verification of information extraction techniques. This paper describes the creation of standard experimental data set as a part of a project financed by Bulgarian National Science Fund. This local thematic spectral library is going to allow direct comparability of data from various sources including from available spectral databases. The deliverables are summarized in chapters including information about the used spectrometers and the methodology of measurements; the description of studied land covers in the points of measurements; the additional information such as GPS coordinates and atmospheric conditions for the monitored land covers in appropriated format. It should be noted that the spectral measurements are made with different instruments with proper calibration sources. The final result is creating of a thematic spectral library in local scale as an open access database. For user friendly access to the library without specific programs, simple text versions of the spectral data, their visualizations, and text files in HyperText Markup Language (HTML) format with the metadata and the additional information is used. The authors intended to propose the possibility for exploiting the spectral data from specialists working in different areas following the procedures for accessing the thematic spectral database and downloading the spectral data. This work is supported by “National Science Fund” in Bulgaria under Contract number KP-06-M27/2.
Learning to Selectively Update State Neurons in Recurrent Networks

Recurrent Neural Networks (RNNs) are the state-of-the-art approach to sequential learning. However, standard RNNs use the same amount of computation to generate their hidden states at each timestep, regardless of the input data. Recent works have begun to tackle this rigid assumption by imposing a priori-determined patterns for updating the states at each step. These approaches could lend insights into the dynamics of RNNs and possibly speed up inference. However, the pre-determined nature of the current update strategies limits their application. To overcome this, we instead design the first fully-learned approach, SA-RNN, that augments any RNN by predicting discrete update patterns at the fine granularity of individual hidden state neurons. This is achieved through the parameterization of a distribution of update-likelihoods driven by the input data. Unlike related methods, our approach imposes no assumptions on the structure of the update patterns. Better yet, our method adapts its update patterns online, allowing different dimensions to be updated conditionally based on the input. To learn which dimensions to update, the model solves a multi-objective optimization problem, maximizing task performance while minimizing the number of updates based on a unified control. Using five publicly-available datasets spanning three sequential learning settings, we demonstrate that our method consistently achieves higher accuracy with fewer updates compared to state-of-the-art alternatives. We also show the benefits of learning to sparsely-update a large hidden state as opposed to densely-update a small hidden state. As an added benefit, our method can be directly applied to a wide variety of models containing RNN architectures.
User Story Driven Adaptive Planning Framework in Personal Daily Context

This paper proposes a just-in-time approach to introduce adaptive planning in personal daily context. Adaptive planning is known to be effective in agile development, which is expected to be useful for our daily life as well. The chatbot-based approach has been proposed for modeling personal daily context as user story graph. This paper extends this approach by adopting the design of sprint planning. To evaluate the effectiveness of the proposed method, a short-term real-world simulation using a storytelling interface is conducted. The result shows that the proposed adaptive planning framework could lead to better decision-making during individual's daily life.
Digitally Reconstructed Radiograph Generation for Enabling AI/ML in Medical Imaging

Now-a-days artificial intelligence has huge impact on medical field and outcompeted the conventional methods that are being used. In clinical fluoroscopy procedures most of the annotated data is not archived. Hence there is lack of meaningful labelled data in radiography. To generate this data from three dimensional annotated computed tomography volume using ray casting is known as Digitally Reconstructed Radiograph (DRR). This radiograph should be very similar to the real one by adding functionality such as attenuation and different parameters. This parameters are similar to the actual system pipeline of generating x-ray. The parameters include kV, mA, rotation and translation. The pipeline also consists of scatter estimation, dynamic range management, noise generation, etc. Multiple DRRs can be generated using the proposed pipeline with given transformations and learning targets. This work focuses on improving quality and speed of DRR generation. The notable amount of diverse data can be retrieved by applying data augmentation on the given small data. Thus this DRRs can be used for training, testing and validating different task based specific AI/ML models. These DRRs can be used for various tasks in x-ray imaging as classification, anatomical landmark detection, vessel segmentation, etc. The proposed solution in this work has taken 1.2 seconds on average to generate DRR of image size 1000*1000 pixel. This implies the speed of writing as 1.5 µs per pixel.
Real-Time Object Recognition for Football Field Landmark Detection Based on Deep Neural Networks

The main goal of this study is to detect the landmark of football fields with a real-time object recognition system based on deep neural networks. The deep neural networks used are YOLO (You Only Look Once). The deep learning of YOLO can detect and recognize objects robustly in a high speed. By using this method, the robot can robustly detect and recognize the landmark of football fields in images with size of 608 × 608 pixels at 16 frames per second with MAP (Mean Average Precision) and IoU (Intersect over Union) with 97.60% and 81.36% accuracy.
Projective Moment Invariants

Moment invariants are viewed as a kind of ideal features for measureing the objects' similarity. Among them, the most difficult kind of invariants is projective invariants. This paper presents a new form of projective moment invariant which is based on the projective differential invariant.