OpenRaiser/Intern-Atlas huggingface dataset:
Progressive Depth Learning for Single Image Dehazing
Venue: arXiv cs.CV 2021
Year: 2021

The formulation of the hazy image is mainly dominated by the reflected lights and ambient airlight. Existing dehazing methods often ignore the depth cues and fail in distant areas where heavier haze disturbs the visibility. However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase. In turn, the good transmission estimation could facilitate the depth estimation for hazy images. In this paper, a deep end-to-end model that iteratively estimates image depths and transmission maps is proposed to perform an effective depth prediction for hazy images and improve the dehazing performance with the guidance of depth information. The image depth and transmission map are progressively refined to better restore the dehazed image. Our approach benefits from explicitly modeling the inner relationship of image depth and transmission map, which is especially effective for distant hazy areas. Extensive results on the benchmarks demonstrate that our proposed network performs favorably against the state-of-the-art dehazing methods in terms of depth estimation and haze removal.
Transitioning from Blackboard to Moodle amidst Natural Disaster: Faculty and Students Perceptions
Venue: arXiv cs.CY 2021
Year: 2021

Higher educational institutions continuously look for ways to improve the quality of their eLearning services and adapt learning solutions to suit the needs of the institution. During the 2016 Fall Semester, a university located in the Southern part of United States decided to transition from the Blackboard learning management system (LMS) to the Moodle learning management system. Typically such a transition presents a huge challenge for the University staff, faculty, and students. Additionally, on August 2016, what CNN themedthe worst natural disaster, to strike the United States since Hurricane Sandy, occurred in Louisiana during the transition. This led to massive disruptions in activities throughout the state. This paper examines the perceptions of both faculty and student on the transition from one LMS to another and also what impact, if any, the natural disaster had on the process. Faculty and students were surveyed to gain understanding of how they perceived the transitioning process, their perception of both systems, their preferences, and why. Furthermore, we identified issues peculiar to transitioning during a natural disaster. The results of this study can be used to anticipate issues that may be associated with transitioning from one LMS to the other and issues peculiar to transitioning amidst a natural disaster. It can also be used to identify areas for improvement.
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown
Venue: arXiv cs.LG 2021
Year: 2021

Air pollution has a wide range of implications on agriculture, economy, road accidents, and health. In this paper, we use novel deep learning methods for short-term (multi-step-ahead) air-quality prediction in selected parts of Delhi, India. Our deep learning methods comprise of long short-term memory (LSTM) network models which also include some recent versions such as bidirectional-LSTM and encoder-decoder LSTM models. We use a multivariate time series approach that attempts to predict air quality for 10 prediction horizons covering total of 80 hours and provide a long-term (one month ahead) forecast with uncertainties quantified. Our results show that the multivariate bidirectional-LSTM model provides best predictions despite COVID-19 impact on the air-quality during full and partial lockdown periods. The effect of COVID-19 on the air quality has been significant during full lockdown; however, there was unprecedented growth of poor air quality afterwards.
Patterns of Cognition: Cognitive Algorithms as Galois Connections Fulfilled by Chronomorphisms On Probabilistically Typed Metagraphs
Venue: arXiv cs.AI 2021
Year: 2021

It is argued that a broad class of AGI-relevant algorithms can be expressed in a common formal framework, via specifying Galois connections linking search and optimization processes on directed metagraphs whose edge targets are labeled with probabilistic dependent types, and then showing these connections are fulfilled by processes involving metagraph chronomorphisms. Examples are drawn from the core cognitive algorithms used in the OpenCog AGI framework: Probabilistic logical inference, evolutionary program learning, pattern mining, agglomerative clustering, pattern mining and nonlinear-dynamical attention allocation. The analysis presented involves representing these cognitive algorithms as recursive discrete decision processes involving optimizing functions defined over metagraphs, in which the key decisions involve sampling from probability distributions over metagraphs and enacting sets of combinatory operations on selected sub-metagraphs. The mutual associativity of the combinatory operations involved in a cognitive process is shown to often play a key role in enabling the decomposition of the process into folding and unfolding operations; a conclusion that has some practical implications for the particulars of cognitive processes, e.g. militating toward use of reversible logic and reversible program execution. It is also observed that where this mutual associativity holds, there is an alignment between the hierarchy of subgoals used in recursive decision process execution and a hierarchy of subpatterns definable in terms of formal pattern theory.
LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs
Venue: arXiv cs.DB 2021
Year: 2021

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated predicates, and various types of queries involving multiple joins, render cardinality estimation a particularly challenging task. In this paper, we develop a framework, termed LMKG, that adopts deep learning approaches for effectively estimating the cardinality of queries over RDF graphs. We employ both supervised (i.e., deep neural networks) and unsupervised (i.e., autoregressive models) approaches that adapt to the subgraph patterns and produce more accurate cardinality estimates. To feed the underlying data to the models, we put forward a novel encoding that represents the queries as subgraph patterns. Through extensive experiments on both real-world and synthetic datasets, we evaluate our models and show that they overall outperform the state-of-the-art approaches in terms of accuracy and execution time.
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification
Venue: arXiv cs.NE 2021
Year: 2021

With the continued innovations of deep neural networks, spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption.However, for continuous data values, they must employ a coding process to convert the values to spike trains.Thus, they have not yet exceeded the performance of artificial neural networks (ANNs), which handle such values directly.To this end, we combine an ANN and an SNN to build versatile hybrid neural networks (HNNs) that improve the concerned performance.To qualify this performance, MNIST and CIFAR-10 image datasets are used for various classification tasks in which the training and coding methods changes.In addition, we present simultaneous and separate methods to train the artificial and spiking layers, considering the coding methods of each.We find that increasing the number of artificial layers at the expense of spiking layers improves the HNN performance.For straightforward datasets such as MNIST, it is easy to achieve the same performance as ANNs by using duplicate coding and separate learning.However, for more complex tasks, the use of Gaussian coding and simultaneous learning is found to improve the accuracy of HNNs while utilizing a smaller number of artificial layers.
FASTEN: Fair and Secure Distributed Voting Using Smart Contracts
Venue: arXiv cs.CR 2021
Year: 2021

Electing democratic representatives via voting has been a common mechanism since the 17th century. However, these mechanisms raise concerns about fairness, privacy, vote concealment, fair calculations of tally, and proxies voting on their behalf for the voters. Ballot voting, and in recent times, electronic voting via electronic voting machines (EVMs) improves fairness by relying on centralized trust. Homomorphic encryption-based voting protocols also assure fairness but cannot scale to large scale elections such as presidential elections. In this paper, we leverage the blockchain technology of distributing trust to propose a smart contract-based protocol, namely, \proto. There are many existing protocols for voting using smart contracts. We observe that these either are not scalable or leak the vote tally during the voting stage, i.e., do not provide vote concealment. In contrast, we show that FASTEN preserves voter's privacy ensures vote concealment, immutability, and avoids double voting. We prove that the probability of privacy breaches is negligibly small. Further, our cost analysis of executing FASTEN over Ethereum is comparable to most of the existing cost of elections.
Liberalisation of the International Gateway and Internet Development in Zambia: The Genesis, Opportunities, Challenges, and Future Directions
Venue: arXiv cs.NI 2021
Year: 2021

Telecommunication reforms in Zambia and the subsequent liberalisation of the international gateway was perceived as one of the means of promoting social and economic growth in both the urban and rural areas of the country. The outcome of this undertaking propelled the rapid development of Internet which has evidently brought about unprecedented paradigm shifts in the use of Information and Communication Technologies (ICTs). It is indisputable that ICTs, and the Internet in particular, have revolutionalised the way we communicate today. Furthermore, the penetration of ICTs to other spheres of our daily lives is evidence enough that the impacts thereof go beyond mere communicative facets of our lives. However, many challenges arose in the implementation of telecommunications reforms. In order to achieve the status quo, government had to make strategic liberalisation policies in the telecoms sector that saw the opening up of the international communication gateways to the private sector. This is in tandem with the fact that the relationship between government (through its formulation of policies and regulations) and other stakeholders determines the ability of a country to generate and use advanced knowledge for industrial competitiveness. As such, in this paper, we present the genesis and evaluate the impacts associated with the telecommunications reforms and the subsequent liberalisation of international communication gateways, and Internet development in Zambia. We further consider the challenges this has brought about and discuss possible future directions. This is helpful in forecasting the future landscape of the ICT sector considering that the country seeks to achieve universal coverage of both Internet and communication facilities to all Zambians across the country.
Towards Immersive Virtual Reality Simulations of Bionic Vision
Venue: arXiv cs.HC 2021
Year: 2021

Bionic vision is a rapidly advancing field aimed at developing visual neuroprostheses ('bionic eyes') to restore useful vision to people who are blind. However, a major outstanding challenge is predicting what people 'see' when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which is difficult to simulate on a computer screen. In addition, many computational models of bionic vision lack biological realism. To address these challenges, we propose to embed biologically realistic models of simulated prosthetic vision (SPV) in immersive virtual reality (VR) so that sighted subjects can act as 'virtual patients' in real-world tasks.
EvoK: Connecting loved ones through Heart Rate sharing
Venue: arXiv cs.HC 2021
Year: 2021

In this work, we present EvoK, a new way of sharing one's heart rate with feedback from their close contacts to alleviate social isolation and loneliness. EvoK consists of a pair of wearable prototype devices (i.e., sender and receiver). The sender is designed as a headband enabling continuous sensing of heart rate with aesthetic designs to maximize social acceptance. The receiver is designed as a wristwatch enabling unobtrusive receiving of the loved one's continuous heart rate with multi-modal notification systems.
Survey on Enterprise Internet-of-Things Systems (E-IoT): A Security Perspective
Venue: arXiv cs.CR 2021
Year: 2021

As technology becomes more widely available, millions of users worldwide have installed some form of smart device in their homes or workplaces. These devices are often off-the-shelf commodity systems, such as Google Home or Samsung SmartThings, that are installed by end-users looking to automate a small deployment. In contrast to these "plug-and-play" systems, purpose-built Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI, Savant offer a smart solution for more sophisticated applications (e.g., complete lighting control, A/V management, security). In contrast to commodity systems, E-IoT systems are usually closed source, costly, require certified installers, and are overall more robust for their use cases. Due to this, E-IoT systems are often found in expensive smart homes, government and academic conference rooms, yachts, and smart private offices. However, while there has been plenty of research on the topic of commodity systems, no current study exists that provides a complete picture of E-IoT systems, their components, and relevant threats. As such, lack of knowledge of E-IoT system threats, coupled with the cost of E-IoT systems has led many to assume that E-IoT systems are secure. To address this research gap, raise awareness on E-IoT security, and motivate further research, this work emphasizes E-IoT system components, E-IoT vulnerabilities, solutions, and their security implications. In order to systematically analyze the security of E-IoT systems, we divide E-IoT systems into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and Applications Layer, and Business Layer. We survey attacks and defense mechanisms, considering the E-IoT components at each layer and the associated threats. In addition, we present key observations in state-of-the-art E-IoT security and provide a list of open research problems that need further research.
Synthesizing Irreproducibility in Deep Networks
Venue: arXiv cs.LG 2021
Year: 2021

The success and superior performance of deep networks is spreading their popularity and use to an increasing number of applications. Very recent works, however, demonstrate that modern day deep networks suffer from irreproducibility (also referred to as nondeterminism or underspecification). Two or more models that are identical in architecture, structure, training hyper-parameters, and parameters, and that are trained on exactly the same training data, yield different predictions on individual previously unseen examples. Thus, a model that performs well on controlled test data, may perform in unexpected ways when deployed in the real world, whose data is expected to be similar to the test data. We study simple synthetic models and data to understand the origins of these problems. We show that even with a single nonlinearity and for very simple data and models, irreproducibility occurs. Our study demonstrates the effects of randomness in initialization, training data shuffling window size, and activation functions on prediction irreproducibility, even under very controlled synthetic data. While, as one would expect, randomness in initialization and in shuffling the training examples exacerbates the phenomenon, we show that model complexity and the choice of nonlinearity also play significant roles in making deep models irreproducible.
Polar-Coded Non-Coherent Communication
Venue: arXiv cs.IT 2021
Year: 2021

A polar-coded transmission (PCT) scheme with joint channel estimation and decoding is proposed for channels with unknown channel state information (CSI). The CSI is estimated via successive cancellation (SC) decoding and the constraints imposed by the frozen bits. SC list decoding with an outer code improves performance, including resolving a phase ambiguity when using quadrature phase-shift keying (QPSK) and Gray labeling. Simulations with 5G polar codes and QPSK show gains of up to $2$~dB at a frame error rate (FER) of $10^{-4}$ over pilot-assisted transmission for various non-coherent models. Moreover, PCT performs within a few tenths of a dB to a coherent receiver with perfect CSI. For Rayleigh block-fading channels, PCT outperforms an FER upper bound based on random coding and within one dB of a lower bound.
Dither computing: a hybrid deterministic-stochastic computing framework
Venue: arXiv cs.AR 2021
Year: 2021

Stochastic computing has a long history as an alternative method of performing arithmetic on a computer. While it can be considered an unbiased estimator of real numbers, it has a variance and MSE on the order of $Ω(\frac{1}{N})$. On the other hand, deterministic variants of stochastic computing remove the stochastic aspect, but cannot approximate arbitrary real numbers with arbitrary precision and are biased estimators. However, they have an asymptotically superior MSE on the order of $O(\frac{1}{N^2})$. Recent results in deep learning with stochastic rounding suggest that the bias in the rounding can degrade performance. We proposed an alternative framework, called dither computing, that combines aspects of stochastic computing and its deterministic variants and that can perform computing with similar efficiency, is unbiased, and with a variance and MSE also on the optimal order of $Θ(\frac{1}{N^2})$. We also show that it can be beneficial in stochastic rounding applications as well. We provide implementation details and give experimental results to comparatively show the benefits of the proposed scheme.
MetaDelta: A Meta-Learning System for Few-shot Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks. Naturally, few-shot learning has been one of the most popular applications for meta-learning. However, existing meta-learning algorithms rarely consider the time and resource efficiency or the generalization capacity for unknown datasets, which limits their applicability in real-world scenarios. In this paper, we propose MetaDelta, a novel practical meta-learning system for the few-shot image classification. MetaDelta consists of two core components: i) multiple meta-learners supervised by a central controller to ensure efficiency, and ii) a meta-ensemble module in charge of integrated inference and better generalization. In particular, each meta-learner in MetaDelta is composed of a unique pretrained encoder fine-tuned by batch training and parameter-free decoder used for prediction. MetaDelta ranks first in the final phase in the AAAI 2021 MetaDL Challenge\footnote{https://competitions.codalab.org/competitions/26638}, demonstrating the advantages of our proposed system. The codes are publicly available at https://github.com/Frozenmad/MetaDelta.
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
Venue: arXiv cs.IT 2021
Year: 2021

We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT.
Hide and Seek: Outwitting Community Detection Algorithms
Venue: arXiv cs.SI 2021
Year: 2021

Community affiliation of a node plays an important role in determining its contextual position in the network, which may raise privacy concerns when a sensitive node wants to hide its identity in a network. Oftentimes, a target community seeks to protect itself from adversaries so that its constituent members remain hidden inside the network. The current study focuses on hiding such sensitive communities so that the community affiliation of the targeted nodes can be concealed. This leads to the problem of community deception which investigates the avenues of minimally rewiring nodes in a network so that a given target community maximally hides from a community detection algorithm. We formalize the problem of community deception and introduce NEURAL, a novel method that greedily optimizes a node-centric objective function to determine the rewiring strategy. Theoretical settings pose a restriction on the number of strategies that can be employed to optimize the objective function, which in turn reduces the overhead of choosing the best strategy from multiple options. We also show that our objective function is submodular and monotone. When tested on both synthetic and 7 real-world networks, NEURAL is able to deceive 6 widely used community detection algorithms. We benchmark its performance with respect to 4 state-of-the-art methods on 4 evaluation metrics. Additionally, our qualitative analysis of 3 other attributed real-world networks reveals that NEURAL, quite strikingly, captures important meta-information about edges that otherwise could not be inferred by observing only their topological structures.
Post-hoc Overall Survival Time Prediction from Brain MRI
Venue: arXiv cs.CV 2021
Year: 2021

Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to contain such precise segmentation, those SOTA methods have limited applicability. In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training. Our model uses medical image and patient demographics (represented by age) as inputs to estimate the OS time and to estimate a saliency map that localizes the tumor as a way to explain the OS time prediction in a post-hoc manner. It is worth emphasizing that although our model can localize tumors, it uses only the ground truth OS time as training signal, i.e., no segmentation labels are needed. We evaluate our post-hoc method on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 data set and show that it achieves competitive results compared to pre-hoc methods with the advantage of not requiring segmentation labels for training.