Reviewerly/exHarmony huggingface dataset:
PiTree

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly implemented in client devices, especially mobile phones with very limited resources. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the implementation of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance and scalable framework that can faithfully convert sophisticated ABR algorithms into lightweight decision trees to reduce deployment overhead. We also provide a theoretical upper bound on the optimization loss during the conversion. Evaluation results on three representative ABR algorithms demonstrate that PiTree could faithfully convert ABR algorithms into decision trees with <3% average performance degradation. Moreover, comparing to original implementation solutions, PiTree could save operating expenses for large content providers.
Embodied One-Shot Video Recognition

One-shot learning aims to recognize novel target classes from few examples by transferring knowledge from source classes, under a general assumption that the source and target classes are semantically related but not exactly the same. Based on this assumption, recent work has focused on image-based one-shot learning, while little work has addressed video-based one shot learning. One of the challenges lies in that it is difficult to maintain the disjoint-class assumption for videos, since video clips of target classes may potentially appear in the videos of source classes. To address this issue, we introduce a novel setting, termed as embodied agents based one-shot learning, which leverages synthetic videos produced in a virtual environment to understand realistic videos of target classes. In this setting, we further propose two types of learning tasks: embodied one-shot video domain adaptation and embodied one-shot video transfer recognition. These tasks serve as a testbed for evaluating video related one-shot learning tasks. In addition, we propose a general video segment augmentation method, which significantly facilitates a variety of one-shot learning tasks. Experimental results validate the soundness of our setting and learning tasks, and also show the effectiveness of our augmentation approach to video recognition in the small-sample size regime.
From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people's daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners' fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.
AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research

Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency. In this paper, we contribute a novel self-supervised auxiliary task, i.e., Terminal Prediction (TP), estimating temporal closeness to terminal states for episodic tasks. The intuition is to help representation learning by letting the agent predict how close it is to a terminal state, while learning its control policy. Although TP could be integrated with multiple algorithms, this paper focuses on Asynchronous Advantage Actor-Critic (A3C) and demonstrating the advantages of A3C-TP. Our extensive evaluation includes: a set of Atari games, the BipedalWalker domain, and a mini version of the recently proposed multi-agent Pommerman game. Our results on Atari games and the BipedalWalker domain suggest that A3C-TP outperforms standard A3C in most of the tested domains and in others it has similar performance. In Pommerman, our proposed method provides significant improvement both in learning efficiency and converging to better policies against different opponents.
Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential

This paper presents a new simplified method for analyzing the availability of photovoltaic potential on roofs. Photovoltaic systems on roofs are widespread as they represent a sustainable and safe investment and, therefore, a means of energy self-sufficiency. With the growth of photovoltaic systems, it is also crucial to correctly evaluate their global efficiency. Thus, this paper presents a comparison between known methods for estimating the photovoltaic potential (as physical, geographic and technical contributions) on a roof and proposes a new simplified method, that takes into account the economic potential of a building that already has installed a photovoltaic system. The measured values of generated electricity of the photovoltaic system were compared with calculated photovoltaic potential. In general, the annual physical, geographic, technical and economic potentials were 1273.7, 1253.8, 14.2 MWh, and 279.1 Wh, respectively. The analysis of all four potentials is essential for further understanding of the sustainable and safe investment in photovoltaic systems.
Neural Multi-Task Learning for Stance Prediction

We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction.In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the stance prediction task.Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.
MULTIMEDIA INTERAKTIF SEBAGAI MEDIA VISUALISASI DASAR-DASAR ANIMASI

The basics of animation are vocational subjects that must be taken and mastered by vocational students majoring in animation.Interactive multimedia is a solution to improve learning activities and be able to provide a real visualization of the material.The use of monotonous e-books can not provide real visualization so that the impact on the lack of understanding of the material being studied, especially animation material that requires visualization.Lack of understanding of the material can have an impact on student learning outcomes.This research focuses on producing interactive multimedia that is valid and suitable for use in learning.The development model uses the Lee and Owens model which consists of analysis, design, development, implementation and evaluation.The results of the validation from the material experts get a validity level of 70%, from media experts by 85%, and from an audience of 81.7%, so that the interactive multimedia developed is said to be valid and feasible.From the results of the implementation based on pre-test and post-test there is an increase in learning outcomes.The results of the pre-test students get an average value of 46.8 of 27 students and get an average value of 72.7 at the time of the post-test.
Zero-Resource Neural Machine Translation with Monolingual Pivot Data

Zero-shot neural machine translation (NMT) is a framework that uses source-pivot and target-pivot parallel data to train a sourcetarget NMT system.An extension to zeroshot NMT is zero-resource NMT, which generates pseudo-parallel corpora using a zeroshot system and further trains the zero-shot system on that data.In this paper, we expand on zero-resource NMT by incorporating monolingual data in the pivot language into training; since the pivot language is usually the highest-resource language of the three, we expect monolingual pivot-language data to be most abundant.We propose methods for generating pseudo-parallel corpora using pivotlanguage monolingual data and for leveraging the pseudo-parallel corpora to improve the zero-shot NMT system.We evaluate these methods for a high-resource language pair (German-Russian) using English as the pivot.We show that our proposed methods yield consistent improvements over strong zero-shot and zero-resource baselines and even catch up to pivot-based models in BLEU (while not requiring the two-pass inference that pivot models require).
Onion Ring ORAM

Oblivious RAM (ORAM) is a cryptographic primitive that allows a client to hide access pattern to its data encrypted and stored at a remote server. Traditionally, ORAM algorithms assume the server acts purely as a storage device. Under this assumption, ORAM has at least log(N) bandwidth blowup for N data entries. After three decades of improvements, ORAM algorithms have reached the optimal logarithmic bandwidth blowup. Nonetheless, in many practical use-cases a constant bandwidth overhead is desirable. To this purpose, Devadas et al. (TCC 2016) formalized the server computation model for ORAM and proposed Onion ORAM which relies on homomorphic computation to achieve constant worst-case bandwidth blowup. This line of work is generally believed to be purely theoretical, due to the large overheads of homomorphic computation. In this paper, we present Onion Ring ORAM, the first efficient constant bandwidth ORAM scheme in the single server model, based on the Onion ORAM construction and the leveled version of the TFHE scheme by Chillotti et al.. We propose a series of improvements, most notably including a more efficient homomorphic permutation protocol. We implement Onion Ring ORAM and show that it can outperform state-of-the-art logarithmic-bandwidth ORAM like Path ORAMs and Ring ORAM when the network throughput is limited. Under one setting, our construction reduces monetary cost per access by 40% and end-to-end latency by 35% over Ring ORAM.
Exploiting Discourse-Level Segmentation for Extractive Summarization

Extractive summarization selects and concatenates the most essential text spans in a document.Most, if not all, neural approaches use sentences as the elementary unit to select content for summarization.However, semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries.In this work, we propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document.We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer.Experiment results on the CNN/Daily Mail dataset show that discourse-level segmentation is effective in both cases.In particular, we achieve state-of-the-art performance when discourse-level segmentation is combined with our adapted contextual representation model.
Neural Attention Frameworks for Explainable Recommendation

Neural attention, an emerging technique used to identify important inputs within neural networks, have become increasingly popular in the area of recommender systems. Not only allowing to better identify what defines users and items, attention-based recommender systems are further able to provide accompanying explanations. However, these representations usually capture only part of users' preferences and items' attributes, resulting in limited reasoning and accuracy. We therefore propose Dual Attention Recommender with Items and Attributes (DARIA), a novel approach able to combine two dependable neural attention mechanisms to better justify its suggestions. Utilizing the personalized history of users, DARIA identifies the most relevant past activities while considering the real-world features that contributed to the similarity. In addition, we adopt the novel approach of self-attention and introduce Self-Attention Recommender based on Attributes and History (SARAH). As a variation to DARIA, SARAH utilizes two self-attention components to describe users by their most characteristic past activities and items by their best depicting attributes. Various experiments establish the significant improvement of SARAH and DARIA over seven key baselines in diverse recommendation settings. By comparing our two proposed frameworks, we demonstrate the potential benefit of applying self-attention in different scenarios.
A Novel Spatial-Temporal Graph for Skeleton-based Driver Action Recognition

Driver irregular operations have long been recognized as the main contributing factors in traffic accidents. Therefore, early and accurate identification of driver actions can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. This paper studies skeletonbased driver action recognition using spatial-temporal graph and genetically-weighted algorithm. We first determine the body pose of the driver by selectively sampling the informative frames in a video, which includes driver joints with high confidence scores and joints position coordinate obtained in pose estimation algorithm. Afterward, we use the skeleton-based graph in the spatial-temporal domains to estimate driver's joints position, and these dynamic skeletons of the captured poses are used as inputs to the Graph Convolutional Networks (GCNs). Finally, the genetically-weighted algorithm is used to vote for the joint point with the highest score of association and significant position change, and we adopt the correlation between the sensory input and the impending driver behavior to determine the driver's detailed actions. We experiment in the Kinetics, NTURGB+D and StateFarm datasets. The results show that our method has high accuracy and strong generalization capability.
Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates

We build the first full pipeline for semantic role labelling of Russian texts.The pipeline implements predicate identification, argument extraction, argument classification (labeling), and global scoring via integer linear programming.We train supervised neural network models for argument classification using Russian semantically annotated corpus -FrameBank.However, we note that this resource provides annotations only to a very limited set of predicates.We combat the problem of annotation scarcity by introducing two models that rely on different sets of features: one for "known" predicates that are present in the training set and one for "unknown" predicates that are not.We show that the model for "unknown" predicates can alleviate the lack of annotation by using pretrained embeddings.We perform experiments with various types of embeddings including the ones generated by deep pretrained language models: word2vec, FastText, ELMo, BERT, and show that embeddings generated by deep pretrained language models are superior to classical shallow embeddings for argument classification of both "known" and "unknown" predicates.
On the difficulties of being rigorous in environmental geochemistry studies: some recommendations for designing an impactful paper

There have been numerous environmental geochemistry studies using chemical, geological, ecological, and toxicological methods but each of these fields requires more subject specialist rigour than has generally been applied so far. Field-specific terminology has been misused and the resulting interpretations rendered inaccurate. In this paper, we propose a series of suggestions, based on our experience as teachers, researchers, reviewers, and editorial board members, to help authors to avoid pitfalls. Many scientific inaccuracies continue to be unchecked and are repeatedly republished by the scientific community. These recommendations should help our colleagues and editorial board members, as well as reviewers, to avoid the numerous inaccuracies and misconceptions currently in circulation and establish a trend towards greater rigour in scientific writing.
Dual axis solar tracker with IoT monitoring system using arduino

This project aims to develop dual axis solar tracker with IOT monitoring system using Arduino. Generally, solar energy is the technology to get useful energy from sunlight. Solar energy has been used in many traditional technologies over the centuries and has been widely used in the absence of other energy supplies. Its usefulness is widespread when awareness of the cost of the environment and the supply is limited by other energy sources such as fuel. The solar tracking system is the most effective technology to improve the efficiency of solar panels by tracking and following the sun's movement. With the help of this system, solar panels can improve the way of sunlight detection so that more electricity can be collected as solar panels can maintain a sunny position. Thus the project discusses the development of two-axis solar-tracking developers using Arduino Uno as main controller the system. For develops this project, four light-dependent resistors (LDRs) have been used for sunlight detection and a maximum light intensity. Two servo motors have been used to rotate the solar panel according to the sun's light source detected by the LDR. Next a WIFI ESP8266 device is used as an intermediary between device and IOT monitoring system. The IOT monitoring system is a website that functions to store data. The efficiency of this system has been tested and compared with a single axial solar tracker. As a result, the two-axis solar tracking system generates more power, voltage and current.
Natural language processing for populating lung cancer clinical research data

Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique.In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports.Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively.This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.
Comparison of Different Clustering Algorithms via Genetic Algorithm for VRPTW

In this paper, Vehicle Routing Problem with Time Windows (VRPTW) with known customer demands, a central depot and a set of vehicles with limited capacity, is considered.The objectives are both to minimize the total distance and the total waiting time of the vehicles while capacity and time windows constraints are secured.The applied solution techniques consist of three steps: clustering, routing and optimizing.By using K-means, Centroid-based heuristic, DBSCAN and SNN clustering algorithms in the initial population generation phase of genetic algorithm, the customers are divided into feasible clusters.Then feasible routes are constructed for each cluster.Lastly, the feasible route solutions are taken as the initial population and genetic algorithm is utilized for the optimization.A set of well-known benchmark data is used to compare the obtained results.According to the results of the study it is observed that using K-means clustering algorithm in generating the initial population of the genetic algorithm is more effective for the handled problem.
A modular cGAN classification framework: Application to colorectal tumor detection

Automatic identification of tissue structures in the analysis of digital tissue biopsies remains an ongoing problem in digital pathology. Common barriers include lack of reliable ground truth due to inter- and intra- reader variability, class imbalances, and inflexibility of discriminative models. To overcome these barriers, we are developing a framework that benefits from a reliable immunohistochemistry ground truth during labeling, overcomes class imbalances through single task learning, and accommodates any number of classes through a minimally supervised, modular model-per-class paradigm. This study explores an initial application of this framework, based on conditional generative adversarial networks, to automatically identify tumor from non-tumor regions in colorectal H&E slides. The average precision, sensitivity, and F1 score during validation was 95.13 ± 4.44%, 93.05 ± 3.46%, and 94.02 ± 3.23% and for an external test dataset was 98.75 ± 2.43%, 88.53 ± 5.39%, and 93.31 ± 3.07%, respectively. With accurate identification of tumor regions, we plan to further develop our framework to establish a tumor front, from which tumor buds can be detected in a restricted region. This model will be integrated into a larger system which will quantitatively determine the prognostic significance of tumor budding.