Reviewerly/exHarmony huggingface dataset:
FreeSOLO: Learning to Segment Objects without Annotations

Instance segmentation is a fundamental vision task that aims to recognize and segment each object in an image. However, it requires costly annotations such as bounding boxes and segmentation masks for learning. In this work, we propose a fully unsupervised learning method that learns class-agnostic instance segmentation without any annotations. We present FreeSOLO, a self-supervised instance segmentation framework built on top of the simple instance segmentation method SOLO. Our method also presents a novel localization-aware pre-training framework, where objects can be discovered from complicated scenes in an unsupervised manner. FreeSOLO achieves 9.8% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP_{50}$</tex> on the challenging COCO dataset, which even outperforms several segmentation proposal methods that use manual annotations. For the first time, we demonstrate unsupervised class-agnostic instance segmen-tation successfully. FreeSOLO's box localization significantly outperforms state-of-the-art unsupervised object de-tection/discovery methods, with about 100% relative improvements in COCO AP. FreeSOLO further demonstrates superiority as a strong pre-training method, outperforming state-of-the-art self-supervised pre-training methods by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$+9.8\%$</tex> AP when fine-tuning instance segmentation with only 5% COCO masks. Code is available at: github.com/NVlabs/FreeSOLO
Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT efforts for EEWS. Fifth, it proposes a generic EEWS architecture based on IoT and ML. Finally, the paper addresses the application of ML for earthquake parameters’ observations leading to an efficient EEWS.
Distribution Alignment Using Complement Entropy Objective and Adaptive Consensus-Based Label Refinement For Partial Domain Adaptation

In this work, we address a realistic case of unsupervised domain adaptation, where the source label set subsumes that of the target. This relaxation in the requirement of an identical label set assumption, as witnessed in the standard closed-set variant, poses a challenging obstacle of negative transfer that potentially misleads the learning process from the intended target classification objective. To counteract this issue, we propose a novel framework for a partial domain adaptation setup that enforces domain and category-level alignments through optimization of intra- and inter-class distances, uncertainty suppression on classifier predictions, and target supervision with an adaptive consensus-based sample filtering. In this work, we aim to modify the latent space arrangement where samples from identical classes are forced to reside in close proximity while that from distinct classes are well separated in a domain-agnostic fashion. In addition, the proposed model addresses a challenging issue of uncertainty propagation by employing a complement entropy objective that requires the incorrect classes to have uniformly distributed low-prediction probabilities. Target supervision is ensured by employing a robust technique for adaptive pseudo-label generation using a nonparametric classifier. The methodology employs a strategy that permits supervision from target samples with prediction probabilities higher than an adaptive threshold. We conduct experiments involving a range of partial domain adaptation tasks on two benchmark datasets to thoroughly assess the proposed model’s performance against the state-of-the-art methods. In addition, we performed an ablation study to validate the necessity of the incorporated modules and highlight their contribution to the proposed framework. The experimental findings obtained manifest the superior performance of the proposed model when compared to the benchmarks.
The Effects of Mobile Collaborative Activities in a Second Language Course

This research is designed to explore the areas of collaborative learning and the use of smartphones as a support for collaborative learning through a year-long exploratory multiple case study approach integrating both qualitative and quantitative data analysis. Qualitative exploratory interviews are combined with Multidimensional Scaling Analysis to provide a detailed image of students' mobile use during collaborative activities. The Multidimensional Scaling (MDS) reliability is supported by a second resampling that produced similar results independent of time of subjects. The results are triangulated across the qualitative and quantitative data and key issues are interpreted and discussed. The results indicate that the introduction of mobile access collaborative homework to a second language English class in Japan does have observable effects on students, including changes in use of space, time and method for mobile collaborative homework.
Participation Maximization Based on Social Influence in Online Discussion Forums

In online discussion forums, users are more motivated to take part in discussions when observing other users’ participation—the effect of social influence among forum users. In this paper, we study how to utilize social influence for increasing the overall forum participation. To this end, we propose a mechanism to maximize user influence and boost participation by displaying forum threads to users. We formally define the participation maximization problem, and show that it is a special instance of the social welfare maximization problem with submodular utility functions and it is NP-hard. However, generic approximation algorithms is impracticable for real-world forums due to time complexity. Thus we design a heuristic algorithm, named Thread Allocation Based on Influence (TABI), to tackle the problem. Through extensive experiments using a dataset from a real-world online forum, we demonstrate that TABI consistently outperforms all other algorithms in maximizing participation. The results of this work demonstrates that current recommender systems can be made more effective by considering future influence propagations. The problem of participation maximization based on influence also opens a new direction in the study of social influence.
Position Paper: Dijkstra's Algorithm versus Uniform Cost Search or a Case Against Dijkstra's Algorithm

Dijkstra's single-source shortest-path algorithm (DA) is one of the well-known, fundamental algorithms in computer science and related fields. DA is commonly taught in undergraduate courses. Uniform-cost search (UCS) is a simple version of the best-first search scheme which is logically equivalent to DA. In this paper I compare the two algorithms and show their similarities and differences. I claim that UCS is superior to DA in almost all aspects. It is easier to understand and implement. Its time and memory needs are also smaller. The reason that DA is taught in universities and classes around the world is probably only historical. I encourage people to stop using and teaching DA, and focus on UCS only.
The Rise of Potential Fields in Real Time Strategy Bots

&#x0D; &#x0D; &#x0D; Bots for Real Time Strategy (RTS) games are challenging to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. We show that the use of potential fields for implementing a bot for a real time strategy game gives us a very competitive, configurable, and non-conventional solution.&#x0D; &#x0D; &#x0D;
Global State Evaluation in StarCraft

State evaluation and opponent modelling are important areasto consider when designing game-playing Artificial Intelligence.This paper presents a model for predicting whichplayer will win in the real-time strategy game StarCraft.Model weights are learned from replays using logistic regression.We also present some metrics for estimating player skillwhich can be used a features in the predictive model, includingusing a battle simulation as a baseline to compare playerperformance against.
Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference

Knowledge compilation is a powerful technique for compactly representing and efficiently reasoning about logical knowledge bases. It has been successfully applied to numerous problems in artificial intelligence, such as probabilistic inference and conformant planning. Conditioning, which updates a knowledge base with observed truth values for some propositions, is one of the fundamental operations employed for reasoning. In the propositional setting, conditioning can be efficiently applied in all cases. Recently, people have explored compilation for first-order knowledge bases. The majority of this work has centered around using first-order d-DNNF circuits as the target compilation language. However, conditioning has not been studied in this setting. This paper explores how to condition a first-order d-DNNF circuit. We show that it is possible to efficiently condition these circuits on unary relations. However, we prove that conditioning on higher arity relations is #P-hard. We study the implications of these findings on the application of performing lifted inference for first-order probabilistic models.This leads to a better understanding of which types of queries lifted inference can address.
Path Planning with Adaptive Dimensionality

Path planning quickly becomes computationally hard as the dimensionality of the state-space increases. In this paper, we present a planning algorithm intended to speed up path planning for high-dimensional state-spaces such as robotic arms. The idea behind this work is that while planning in a high-dimensional state-space is often necessary to ensure the feasibilityof the resulting path, large portions of the path have a lower-dimensional structure. Based on this observation, our algorithm iteratively constructs a state-space of an adaptive dimensionality--a state-space that is high-dimensional only where the higher dimensionality is absolutely necessary for finding a feasible path. This often reduces drastically the size of the state-space, and as a result, the planning time and memory requirements. Analytically, we show that our method is complete and is guaranteed to find a solution if one exists, within a specified suboptimality bound. Experimentally, we apply the approach to 3D vehicle navigation (x, y, heading), and to a 7 DOF robotic arm on the Willow Garage’s PR2 robot. The results from our experiments suggest that ourmethod can be substantially faster than some of the state-of-the-art planning algorithms optimized for those tasks.
Modeling Polarizing Topics: When Do Different Political Communities Respond Differently to the Same News?

Political discourse in the United States is getting increasingly polarized. This polarization frequently causes different communities to react very differently to the same news events. Political blogs as a form of social media provide an unique insight into this phenomenon. We present a multitarget, semisupervised latent variable model, MCR-LDA to model this process by analyzing political blogs posts and their comment sections from different political communities jointly to predict the degree of polarization that news topics cause. Inspecting the model after inference reveals topics and the degree to which it triggers polarization. In this approach, community responses to news topics are observed using sentiment polarity and comment volume which serves as a proxy for the level of interest in the topic. In this context, we also present computational methods to assign sentiment polarity to the comments which serve as targets for latent variable models that predict the polarity based on the topics in the blog content. Our results show that the joint modeling of communities with different political beliefs using MCR-LDA does not sacrifice accuracy in sentiment polarity prediction when compared to approaches that are tailored to specific communities and additionally provides a view of the polarization in responses from the different communities.
A New Procedure of Clustering Based on Multivariate Outlier Detection

Clustering is an extremely important task in a wide variety of application domains especially in management and social science research.In this paper, an iterative procedure of clustering method based on multivariate outlier detection was proposed by using the famous Mahalanobis distance.At first, Mahalanobis distance should be calculated for the entire sample, then using T 2 -statistic fix a UCL.Above the UCL are treated as outliers which are grouped as outlier cluster and repeat the same procedure for the remaining inliers, until the variance-covariance matrix for the variables in the last cluster achieved singularity.At each iteration, multivariate test of mean used to check the discrimination between the outlier clusters and the inliers.Moreover, multivariate control charts also used to graphically visualizes the iterations and outlier clustering process.Finally multivariate test of means helps to firmly establish the cluster discrimination and validity.This paper employed this procedure for clustering 275 customers of a famous twowheeler in India based on 19 different attributes of the two wheeler and its company.The result of the proposed technique confirms there exist 5 and 7 outlier clusters of customers in the entire sample at 5% and 1% significance level respectively.
Map-Balance-Reduce: An improved parallel programming model for load balancing of MapReduce

With the advent of the era of big data, the demand of massive data processing applications is also growing. Currently, MapReduce is the most commonly used data processing programming model. However, in some data processing cases, it has some defects. MapReduce programming based on key/value pairs, matches the output of the Map tasks that will be transported to Reduce task nodes. The data with same key can only be processed by a Reduce node. If the data corresponding to a particular key or several keys accounts for most of all data, the Reduce node task will generate unbalanced load. In view of this defect, this paper proposes a new parallel programming model—Map-Balance-Reduce (MBR) programming model. It runs on our improved Hadoop framework and can effectively process the special data with unbalanced keys. This paper is based on two different scheduling, the processing and self-adaption scheduling. These two scheduling are designed to achieve MBR programming model. The actual testing results show that compared with MapReduce programming model, the MBR programming model under Hadoop can achieve the improvement of 9.7% to 17.6% in efficiency when testing data distributes unevenly. Furthermore, when testing conventional even-distributed data, it will only bring 1.02% time cost.
Greening Cloud-Enabled Big Data Storage Forensics: Syncany as a Case Study

The pervasive nature of cloud-enabled big data storage solutions introduces new challenges in the identification, collection, analysis, preservation and archiving of digital evidences. Investigation of such complex platforms to locate and recover traces of criminal activities is a time-consuming process. Hence, cyber forensics researchers are moving towards streamlining the investigation process by locating and documenting residual artefacts (evidences) of forensic value of users activities on cloud-enabled big data platforms in order to reduce the investigation time and resources involved in a real-world investigation. In this paper, we seek to determine the data remnants of forensic value from Syncany private cloud storage service, a popular storage engine for big data platforms. We demonstrate the types and the locations of the artefacts that can be forensically recovered. Findings from this research contribute to an in-depth understanding of cloud-enabled big data storage forensics, which can result in reduced time and resources spent in real-world investigations involving Syncany-based cloud platforms.
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation

Variational auto-encoder (VAE) is a powerful unsupervised learning framework for image generation. One drawback of VAE is that it generates blurry images due to its Gaussianity assumption and thus ℓ2 loss. To allow the generation of high quality images by VAE, we increase the capacity of decoder network by employing residual blocks and skip connections, which also enable efficient optimization. To overcome the limitation of ℓ2 loss, we propose to generate images in a multi-stage manner from coarse to fine. In the simplest case, the proposed multi-stage VAE divides the decoder into two components in which the second component generates refined images based on the course images generated by the first component. Since the second component is independent of the VAE model, it can employ other loss functions beyond the ℓ2 loss and different model architectures. The proposed framework can be easily generalized to contain more than two components. Experiment results on the MNIST and CelebA datasets demonstrate that the proposed multi-stage VAE can generate sharper images as compared to those from the original VAE.
Unsupervised Semantic-Based Aggregation of Deep Convolutional Features

In this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as semantic detectors. Moreover, we propose the effective unsupervised strategy to select some semantic detectors to generate the "soft region proposals," which highlight certain discriminative pattern of objects and suppress the noise of background. The final global SBA representation could then be acquired by aggregating the regional representations weighted by the selected "soft region proposals" corresponding to various semantic content. Our unsupervised SBA is easy to generalize and achieves excellent performance on various tasks. We conduct comprehensive experiments and show that our unsupervised SBA outperforms the state-of-the-art unsupervised and supervised aggregation methods on image retrieval, place recognition, and cloud classification.
An Efficient Method for High Quality and Cohesive Topical Phrase Mining

A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency.
Factors Affecting ENF Capture in Audio

The electric network frequency (ENF) signal is an environmental signature that can be captured in audiovisual recordings made in locations where there is electrical activity. This signal is influenced by the power grid in which the recording is made, and recent work has shown that it can be useful toward a number of forensics and security applications. An under-studied area of ENF research is the factors that can affect the capture of ENF traces in media recordings. Not all recordings made in the areas of electrical activity will carry prominent ENF traces, and the strengths by which the ENF traces are captured can vary from one recording to another. A thorough understanding of the factors that can affect the capture of ENF traces in recordings is essential to understanding the applicability of ENF-based approaches and can help inform related studies in the future. This paper carried out a study on such factors, with a focus on audio signals. The impact of the characteristics of an audio recorder and the environment and manner of recording on the intrinsically captured ENF are shown and analyzed.
Efficient Fine-Grained Data Sharing Mechanism for Electronic Medical Record Systems with Mobile Devices

Sharing digital medical records on public cloud storage via mobile devices facilitates patients (doctors) to get (offer) medical treatment of high quality and efficiency. However, challenges such as data privacy protection, flexible data sharing, efficient authority delegation, computation efficiency optimization, are remaining toward achieving practical fine-grained access control in the Electronic Medical Record (EMR) system. In this work, we propose an innovative access control model and a fine-grained data sharing mechanism for EMR, which simultaneously achieves the above-mentioned features and is suitable for resource-constrained mobile devices. In the model, complex computation is outsourced to public cloud servers, leaving almost no complex computation for the private key generator (PKG), sender and receiver. Additionally, the communication cost of the PKG and users is optimized. Moreover, we develop an extensible library called libabe that is compatible with Android devices, and the access control mechanism is actually deployed on realistic environment, including public cloud servers, a laptop and an inexpensive mobile phone with constrained resources. The experimental results indicate that the mechanism is efficient, practical and economical.