Reviewerly/exHarmony huggingface dataset:
Informed Sampling for Diversity in Concept-to-Text NLG

Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the output. In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. We focus our experiments on concept-to-text generation where models are sensitive to the inclusion of irrelevant words due to the strict relation between input and output. Our analysis shows that previous methods for diversity underperform in this setting, while human evaluation suggests that our proposed method achieves a high level of diversity with minimal effect to the output's fluency and adequacy.
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media

Recent years have witnessed an increasing use of coordinated accounts on social media, operated by misinformation campaigns to influence public opinion and manipulate social outcomes. Consequently, there is an urgent need to develop an effective methodology for coordinated group detection to combat the misinformation on social media. However, existing works suffer from various drawbacks, such as, either limited performance due to extreme reliance on predefined signatures of coordination, or instead an inability to address the natural sparsity of account activities on social media with useful prior domain knowledge. Therefore, in this paper, we propose a coordination detection framework incorporating neural temporal point process with prior knowledge such as temporal logic or pre-defined filtering functions. Specifically, when modeling the observed data from social media with neural temporal point process, we jointly learn a Gibbs-like distribution of group assignment based on how consistent an assignment is to (1) the account embedding space and (2) the prior knowledge. To address the challenge that the distribution is hard to be efficiently computed and sampled from, we design a theoretically guaranteed variational inference approach to learn a mean-field approximation for it. Experimental results on a real-world dataset show the effectiveness of our proposed method compared to the SOTA model in both unsupervised and semi-supervised settings. We further apply our model on a COVID-19 Vaccine Tweets dataset. The detection result suggests the presence of suspicious coordinated efforts on spreading misinformation about COVID-19 vaccines.
Designing a Solar City Using Solar Energy to Generate Energy through Innovative Planning and Energy Simulation System (Kufa University Case Study)

The research indicates the possibility of using solar radiation and its rolein generating electricity through photovoltaic solar systems that do not emit environmental pollutants. To solve the problem of generating electric power from fossil fuels that pollute the environment, the study was conducted in order to answer the following questions:Do solar energy projects have the potential to preserve and sustain the environment?And is there an economic advantage compared to energy sources generated from fossil fuels?Climate characteristics have an impact on its production efficiency and increase in electrical energy consumption, and that there is a relationship between climatic characteristics and solar radiation, and between electric energy produced and climatic characteristics. Because the University of Kufa is one of the first universities to use solar renewable energy in its corridors, it was important to shed light on this aspect for its sustainability in the field of energy.Through the research, we found that the University of Kufa is qualified to be a solar city that produces enough energy and even the surplus to serve its neighboring regions by providing them with produced electric energy.It was found from the feasibility study conducted for the project in terms of cost, age of the system and its location within the university’s borders that it is the right place for installing solar systems because many of the electric towers are located inside the university, and this facilitates the process of connecting them to the governmental electricity grid, and thus it is considered a support and backing for governmental electricity.
Associating Objects with Transformers for Video Object Segmentation

This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios. The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computing resources. To solve the problem, we propose an Associating Objects with Transformers (AOT) approach to match and decode multiple objects uniformly. In detail, AOT employs an identification mechanism to associate multiple targets into the same high-dimensional embedding space. Thus, we can simultaneously process multiple objects' matching and segmentation decoding as efficiently as processing a single object. For sufficiently modeling multi-object association, a Long Short-Term Transformer is designed for constructing hierarchical matching and propagation. We conduct extensive experiments on both multi-object and single-object benchmarks to examine AOT variant networks with different complexities. Particularly, our R50-AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (84.1% J&F), DAVIS 2017 (84.9%), and DAVIS 2016 (91.1%), while keeping more than $3\times$ faster multi-object run-time. Meanwhile, our AOT-T can maintain real-time multi-object speed on the above benchmarks. Based on AOT, we ranked 1st in the 3rd Large-scale VOS Challenge.
Establishing Cyberpsychology at Universities in the Area of Cyber Security

Cyberpsychology deals with the question of the extent to which people interact with technologies, such as computers, and how this can lead to different types of behavior. It also investigates the extent to which behavior and one's own identity can change, depending on the current sphere of activity - either online or offline. Therefore, Cyberpsychology can be a powerful addition to the currently technical oriented Cyber Security. In addition, an overview is provided of what other universities and colleges in and outside Europe offer in the field of cyberpsychology in terms of modules.To the end of the methodology module contents, learning outcomes and the final implementation are discussed. This includes a model with different phases like call for lecturers, preparing transcripts and presentations. Additionally, the European ECTS to hour ratio will be used to determine an appropriate number of working hours.In conclusion, the design of the branch of cyberpsychology at universities is described as future-oriented and promising, while the development at other colleges and universities is undergoing similar changes. Although not necessarily in the field of cyberpsychology, the technical courses of study, and especially computer science, are increasingly oriented towards cyber security and the like.
Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion.

There has been a dramatic increase in the volume of videos and their related content uploaded to the internet. Accordingly, the need for efficient algorithms to analyse this vast amount of data has attracted significant research interest. An action recognition system based upon human body motions has been proven to interpret videos contents accurately. This work aims to recognize activities of daily living using the ST-GCN model, providing a comparison between four different partitioning strategies: spatial configuration partitioning, full distance split, connection split, and index split. To achieve this aim, we present the first implementation of the ST-GCN framework upon the HMDB-51 dataset. We have achieved 48.88 % top-1 accuracy by using the connection split partitioning approach. Through experimental simulation, we show that our proposals have achieved the highest accuracy performance on the UCF-101 dataset using the ST-GCN framework than the state-of-the-art approach. Finally, accuracy of 73.25 % top-1 is achieved by using the index split partitioning strategy.
An Interactive Visualization Tool for Understanding Active Learning

Despite recent progress in artificial intelligence and machine learning, many state-of-the-art methods suffer from a lack of explainability and transparency. The ability to interpret the predictions made by machine learning models and accurately evaluate these models is crucially important. In this paper, we present an interactive visualization tool to elucidate the training process of active learning. This tool enables one to select a sample of interesting data points, view how their prediction values change at different querying stages, and thus better understand when and how active learning works. Additionally, users can utilize this tool to compare different active learning strategies simultaneously and inspect why some strategies outperform others in certain contexts. With some preliminary experiments, we demonstrate that our visualization panel has a great potential to be used in various active learning experiments and help users evaluate their models appropriately.
Temporal-attentive Covariance Pooling Networks for Video Recognition.

For video recognition task, a global representation summarizing the wholecontents of the video snippets plays an important role for the finalperformance. However, existing video architectures usually generate it by usinga simple, global average pooling (GAP) method, which has limited ability tocapture complex dynamics of videos. For image recognition task, there existevidences showing that covariance pooling has stronger representation abilitythan GAP. Unfortunately, such plain covariance pooling used in imagerecognition is an orderless representative, which cannot model spatio-temporalstructure inherent in videos. Therefore, this paper proposes aTemporal-attentive Covariance Pooling(TCP), inserted at the end of deeparchitectures, to produce powerful video representations. Specifically, our TCPfirst develops a temporal attention module to adaptively calibratespatio-temporal features for the succeeding covariance pooling, approximativelyproducing attentive covariance representations. Then, a temporal covariancepooling performs temporal pooling of the attentive covariance representationsto characterize both intra-frame correlations and inter-framecross-correlations of the calibrated features. As such, the proposed TCP cancapture complex temporal dynamics. Finally, a fast matrix power normalizationis introduced to exploit geometry of covariance representations. Note that ourTCP is model-agnostic and can be flexibly integrated into any videoarchitectures, resulting in TCPNet for effective video recognition. Theextensive experiments on six benchmarks using various video architectures showour TCPNet is clearly superior to its counterparts, while having stronggeneralizationability.$\href{https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition}{\textit{Thesource code is publicly available.}}$
The CLEAR Benchmark: Continual LEArning on Real-World Imagery

Continual learning (CL) is widely regarded as crucial challenge for lifelong AI. However, existing CL benchmarks, e.g. Permuted-MNIST and Split-CIFAR, make use of artificial temporal variation and do not align with or generalize to the real-world. In this paper, we introduce CLEAR, the first continual image classification benchmark dataset with a natural temporal evolution of visual concepts in the real world that spans a decade (2004-2014). We build CLEAR from existing large-scale image collections (YFCC100M) through a novel and scalable low-cost approach to visio-linguistic dataset curation. Our pipeline makes use of pretrained vision-language models (e.g. CLIP) to interactively build labeled datasets, which are further validated with crowd-sourcing to remove errors and even inappropriate images (hidden in original YFCC100M). The major strength of CLEAR over prior CL benchmarks is the smooth temporal evolution of visual concepts with real-world imagery, including both high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised learning. We find that a simple unsupervised pre-training step can already boost state-of-the-art CL algorithms that only utilize fully-supervised data. Our analysis also reveals that mainstream CL evaluation protocols that train and test on iid data artificially inflate performance of CL system. To address this, we propose novel "streaming" protocols for CL that always test on the (near) future. Interestingly, streaming protocols (a) can simplify dataset curation since today's testset can be repurposed for tomorrow's trainset and (b) can produce more generalizable models with more accurate estimates of performance since all labeled data from each time-period is used for both training and testing (unlike classic iid train-test splits).
Cyber incident response decision making

In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information.The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
WPFY: Visual Interactive Authoring System for Chinese Classical Poetry

<p indent=0mm>Chinese classical poetry exhibits neat sentence patterns and melodic rhythms, which pose high challenges for creators. Most existing systems focus on generation models without considering user participation. Based on the open-source data of the Gushiwen website, THUNLP-AIPoet, we design and implement WPFY, a visual authoring system for Chinese classical poetry: a configuration view supports specify sentence patterns and emotions; an authoring view automatically generates poetry as the basis, and visualizes the multi-dimensional attributes of poetry such as rhythms and emotions; an auxiliary view presents suggested recommendations. User studies concerning the fluency, emotions, and rhythms of the creations demonstrate the usefulness and effectiveness of WPFY.
Grid Based Realistic Optimal Path Planning

The indoor mobile robot trajectory planning algorithm is widely used across the real world mobile robots applications. There are many types of algorithm that are based on the grid based path planning. Path Planning Algorithms is the major module for deciding the trajectory to move mobile robot to reach the target. The robot is equipped with multiple on body sensors that helps it to calculate the path and shortest distance needed for navigating. Although the moving mobile robot is unaware of the environment components and objects within application boundary, the incorporated sensors explores the in-path objects so as to explore more safest and feasible path. This research paper mainly focused on the development of novel grid based path finding algorithms which overcomes the traditional indoor mobile robot path finding algorithm shortcoming and making more realistic end solution for a variety of robotic and game development application. The proposed novel Realistic Optimal Path Planning Algorithm” (ROPPA), utilize cell pruning techniques to improve the explored path length.  Also this method takes care of future non-collision of mobile robot with in-path obstacle, while utilizing the uniquely designed set of rules. This method improves the overall computation time and path cost requires by 25% compared to traditional path planning algorithm.
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing

In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing. We propose a method to disentangle the overall model vulnerability into the sensitivity to the rotation, the translation, the scale, and local 3D structures. Besides, we also propose metrics to evaluate the spatial smoothness of encoding 3D structures, and the representation complexity of the DNN. Based on such analysis, experiments expose representation problems with classic DNNs, and explain the utility of the adversarial training.
Private well installation trends and correlation of borehole stratigraphy with groundwater arsenic contamination in Kandal Province, Cambodia

Arsenic (As) exposure via contaminated drinking water continues to threaten the health of millions of people worldwide.Despite recent advances in the prediction of As contaminated groundwater 1 and dissemination of the risks of As to human health, wells continue to be installed in regions with known groundwater contamination.In addition, manganese (Mn) exposure through drinking water poses health risks, and often occurs distinct from As contamination in groundwater. 2 We collected basic stratigraphic data (texture and color) at 3 m depth resolution from >500 wells installed in and near a known Asand Mn-affected region in Kandal Province, Cambodia, between 2013-2015.Sediment stratigraphy combined with an existing database of groundwater quality 2 shows trends in well installation (e.g.number of wells installed per year) in areas with known groundwater contamination following government and non-government (NGO)privately organized As education and awareness campaigns, providing .insight on the effect of As awareness programs on new well installations in a known Asaffected region.Further, by combining sediment stratigraphy with groundwater quality data, we also testshow spatial correlation between sediment color, aqueous redox indicators (e.g.Fe(II) and Eh), and groundwater quality (As, Mn).Our hypothesis was that red, orange, and yellow sediments at the screened interval would be proximal to groundwater low in As and with high (oxidizing) Eh.Conversely, wells screened in blue, black or grey sands would be proximal to higher As concentrations and lower Eh.By incorporating results of intensely studied transects and groundwater flow models in the area 3,4 with an expanded description of subsurface stratigraphy we aim to test additional sedimentary controls on the distribution of high As groundwater in Cambodia.
딥 러닝 기법을 이용한 레이더 신호 분류 모델 연구

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning

In low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging problem, as even the most similar sources might lead to unexpected negative transfer results. Thus, ranking methods based on task and text similarity — as suggested in prior work — may not be sufficient to identify promising sources. To tackle this problem, we propose a new approach to automatically determine which and how many sources should be exploited. For this, we study the effects of model transfer on sequence labeling across various domains and tasks and show that our methods based on model similarity and support vector machines are able to predict promising sources, resulting in performance increases of up to 24 F1 points.