Reviewerly/exHarmony huggingface dataset:
Semi-supervised Dual-Branch Network for image classification

In this work, we reveal an essential problem rarely discussed in current semi-supervised learning literatures: the learned feature distribution mismatch problem between labeled samples and unlabeled samples. It is common knowledge that learning from the limited labeled data easily leads to overfitting. However, the difference between the inferred labels of unlabeled data and the ground truths of labeled data may make the learned features of labeled and unlabeled data have different distributions. This distribution mismatch problem may destroy the assumption of smoothness widely used in semi-supervised field, resulting in unsatisfactory performance. In this paper, we propose a novel Semi-supervised Dual-Branch Network (SDB-Net), in which the first branch is trained with labeled and unlabeled data, and the other is trained with the predictions of unlabeled data generated from the first branch only. To avoid the different distributions between ground-truth labels and inferred labels for the unlabeled data, we proposed an effective co-consistency loss to overcome the mismatch problem and a mix-consistency loss to make each branch learn a consistent feature representation. Meanwhile, we designed an augmentation supervised loss for the first branch to further alleviate the mismatch problem. With the designed three kinds of losses, the proposed SDB-Net can be efficiently trained. The experimental results on three benchmark datasets, such as CIFAR-10, CIFAR-100 and SVHN, show the superior performance of the proposed SDB-Net.
Efficient weighted probabilistic frequent itemset mining in uncertain databases

Abstract Uncertain data mining has attracted so much interest in many emerging applications over the past decade. An issue of particular interest is to discover the frequent itemsets in uncertain databases. As an item would not appear in a transaction of such database for certain, several probability models are presented to measure the frequency of an itemset, and the frequent itemset over probabilistic data generally has two different definitions: the expected support‐based frequent itemset and probabilistic frequent itemset. Meanwhile, it is noted that the frequency itself cannot identify useful or meaningful patterns in some scenarios. Other measures such as the importance of items should be also taken into account. To this end, some studies recently have been done on weighted (importance) frequent itemset mining in uncertain databases. However, they are only designed for the expected support‐based frequent itemset, and suffer from low efficiency due to generating too many frequent itemset candidates. To address this issue, we propose a novel weighted probabilistic frequent itemsets (w‐PFIs) algorithm. Moreover, we derive a probability model for the support of a w‐PFI candidate in our method and present three pruning techniques to narrow the search space and remove the unpromising candidates immediately. Extensive experiments have been conducted on both real and synthetic datasets, to evaluate the performance of our w‐PFI algorithm in terms of runtime, accuracy and scalability. Results show that our algorithm yields the best performance among the existing algorithms.
Gated forward refinement network for action segmentation

Action segmentation aims at temporally locating and classifying video segments in long untrimmed videos, which is of particular interest to many applications like surveillance and robotics. While most existing methods tackle this task by predicting frame-wise probabilities and adjusting them via high-level temporal models, recent approaches classify every video frame directly with temporal convolutions. However, there are limits to generate high quality predictions due to ambiguous information in the video frames. In this paper, in order to address the limitations of existing methods in temporal action segmentation task, we propose an end-to-end multi-stage architecture, Gated Forward Refinement Network (G-FRNet). In G-FRNet, each stage makes a prediction that is refined progressively by next stage. Specifically, we propose a new gated forward refinement network to adaptively correct the errors in the prediction from previous stage, where an effective gate unit is used to control the refinement process. Moreover, to efficiently optimize the proposed G-FRNet, we design an objective function that consists of a classification loss and a multi-stage sequence-level refinement loss that incorporates segmental edit score via policy gradient. Extensive evaluation on three challenging datasets (50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset) shows our method achieves state-of-the-art results.
Key Action and Joint CTC-Attention based Sign Language Recognition

Sign Language Recognition (SLR) translates sign language video into natural language. In practice, sign language video, owning a large number of redundant frames, is necessary to be selected the essential. However, unlike common video that describes actions, sign language video is characterized as continuous and dense action sequence, which is difficult to capture key actions corresponding to meaningful sentence. In this paper, we propose to hierarchically search key actions by a pyramid BiLSTM. Specifically, we first construct three BiL-STMs to produce temporal relationships among input video sequence. Then, we associate these BiLSTMs by searching the salient responses in two groups of fixed-scale sliding window and capture key actions. Additionally, in order to balance the sequence alignment and dependency, we propose to jointly train Connectionist Temporal Classification (CTC) and Long Short-Term Memory (LSTM). Experimental results demonstrate the effectiveness of the proposed method.
On the Adversarial Robustness of Robust Estimators

Motivated by recent data analytics applications, we study the adversarial robustness of robust estimators. Instead of assuming that only a fraction of the data points are outliers as considered in the classic robust estimation setup, in this paper, we consider an adversarial setup in which an attacker can observe the whole dataset and can modify all data samples in an adversarial manner so as to maximize the estimation error caused by his attack. We characterize the attacker's optimal attack strategy, and further introduce adversarial influence function (AIF) to quantify an estimator's sensitivity to such adversarial attacks. We provide an approach to characterize AIF for any given robust estimator, and then design optimal estimator that minimizes AIF, which implies it is least sensitive to adversarial attacks and hence is most robust against adversarial attacks. From this characterization, we identify a tradeoff between AIF (i.e., robustness against adversarial attack) and influence function, a quantity used in classic robust estimators to measure robustness against outliers, and design estimators that strike a desirable tradeoff between these two quantities.
Load Distribution Challenges with Virtual Computing

Computing with cloud is the today’s era computational epitome. It is vastly usable figuring technology, i.e., promptly uniting the aforementioned as the upcoming of spread and popular computing. Virtual Figuring renders elastic, as-a-service resource abstraction relies on usage-based payment model, and is rising as an appealing computing epitome. Cloud computing uses the principle of virtualization and is becoming potent supporter of current Internet businesses. Cloud computing accomplishes the needs of immense data storage and majorly parallel computing.
Secure software placement and configuration

Finding the best way to place the components of an application on a set of heterogeneous servers is a challenging task, especially if some components are associated with security requirements. To address security requirements, several security controls may be available, some of them software-based (e.g., encryption), others hardware-based (e.g., trusted execution environments). Security controls may incur widely varying performance overhead. There is a non-trivial interplay between application placement (which component to place on which server) and the configuration of security controls (which security control to activate for which component). On the one hand, placing a component on a secure server may make it unnecessary to use software-based security controls for the component. On the other hand, the overhead of using a specific security control may increase the resource requirements of a component so that it does not fit onto its designated server. Therefore, this paper addresses the joint problem of application placement and configuration of security controls. We formalize the problem and use mixed integer quadratic programming to solve it. A case study is used to demonstrate that the proposed approach can automatically determine the placement and configuration of complex applications.
Spoken Document Retrieval Leveraging Bert-Based Modeling and Query Reformulation

Spoken document retrieval (SDR) has long been deemed a fundamental and important step towards efficient organization of, and access to multimedia associated with spoken content. In this paper, we present a novel study of SDR leveraging the Bidirectional Encoder Representations from Transformers (BERT) model for query and document representations (embeddings), as well as for relevance scoring. BERT has produced extremely promising results for various tasks in natural language understanding, but relatively little research on it is devoted to text information retrieval (IR), let alone SDR. We further tackle one of the critical problems facing SDR, viz. a query is often too short to convey a user's information need, via the process of pseudo-relevance feedback (PRF), showing how information cues induced from PRF can be aptly incorporated into BERT for query expansion. In addition, such query reformulation through PRF also works in conjunction with additional augmentation of lexical features and confidence scores into the document embeddings learned from BERT. The merits of our approach are attested through extensive sets of experiments, which compare it with several classic and cutting-edge (deep learning-based) retrieval approaches.
Cybersecurity in the Blockchain Era : A Survey on Examining Critical Infrastructure Protection with Blockchain-Based Technology

Cybercriminals and nation-state hackers are investing heavily in strategies devised to disrupt critical infrastructure systems. Nearly one out of two Industrial Control System (ICS) computers in the energy sector had been impacted by malicious cyber activity in 2018. With threats increasing and attack vectors expanding, critical infrastructure sectors like energy need to implement innovative technology to ensure Critical Infrastructure Protection. This study reviewed blockchain research published between 2015 and 2019 in order to examine the development of blockchain, evaluate current applications of blockchain in the energy sector, and propose additional opportunities for the application of blockchain technology. Quantitative analysis revealed that blockchain-based cybersecurity research and application in the energy sector is increasing at an annual average growth rate of 169% since 2015 with intensified focus on Internet of Things (IoT) and Smart Grid infrastructures. Computed projections forecast a continued increase in the research and application of IoT and Smart Grids. Calculations indicate that nearly 50% of the new research will be in blockchain-based cybersecurity solutions such as cryptographic communications and secure email. Qualitative analysis indicates potential for blockchain- based cybersecurity solutions to provide enhanced Critical Infrastructure Protection in those sectors against pervasive phishing and social engineering threats.
A Randomized Algorithm for Load Balancing in Containerized Cloud

Cloud computing is one of the highly discussed topics in the field of Internet and communication technology. It is responsible for the on-demand provision of computing resources, mainly data and computing power to the end-users. More than one server works together in a cloud network. So, the incoming request for resources must be distributed among all servers in the network for better performance. The process of efficiently distributing incoming tasks and sharing the workload among a group of servers is called load balancing. In this paper, we propose a randomized algorithm for load balancing in a containerized cloud. The approach we have used is called Balls into Bins via Local Search. In our algorithm, we have considered tasks as balls and servers as bins. First, we construct a fully connected undirected graph of nodes(server) and then convert it to a minimum edge weight graph to reduce the network cost. Our experimental result shows that the load is distributed among all servers almost equally. The difference between the highest and lowest workload in the network is minimized.
Shared Tasks on Authorship Analysis at PAN 2020

The paper gives a brief overview of the four shared tasks that are to be organized at the PAN 2020 lab on digital text forensics and stylometry, hosted at CLEF conference. The tasks include author profiling, celebrity profiling, cross-domain author verification, and style change detection, seeking to advance the state of the art and to evaluate it on new benchmark datasets.
Rate-Constrained Shaping Codes for Structured Sources

Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor - the ratio of the expected codeword length to the length of the input source word - and the minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence.
Generative Datalog with Continuous Distributions

Arguing for the need to combine declarative and probabilistic programming, Bárány et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a "purely declarative probabilistic programming language." We revisit this language and propose a more foundational approach towards defining its semantics. It is based on standard notions from probability theory known as stochastic kernels and Markov processes. This allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by Bárány et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, ICDT 2020), and we show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.
Defending Graph Convolutional Networks Against Adversarial Attacks

The interconnection of social, email, and media platforms enables adversaries to manipulate networked data and promote their malicious intents. This paper introduces graph neural network architectures that are robust to perturbed networked data. The novel network utilizes a randomization layer that performs link-dithering (LD) by adding or removing links with probabilities selected to boost robustness. The resultant link-dithered auxiliary graphs are leveraged by an adaptive (A)GCN that performs SSL. The proposed robust LD-AGCN achieves performance gains relative to GCNs under perturbed network data.
Dynamic Weighted Hybrid Recommender Systems

Recommender Systems (RSs) have emerged since the mid-90s for dealing with the problem of information overload. They are commonly defined as software tools and techniques that serve as an assistant providing suggestions to the users. The two most familiar recommendation techniques are probably Collaborative filtering (CF) and Content-based filtering (CBF). Whereas CF computes recommendations based on past ratings of users with similar preferences, CBF assumes that each user operates independently, thus exploits only information derived from item features. Technically speaking, the performance of every single recommendation algorithm is limited and each has its own strengths and weaknesses, so recently more attentions are paid to the hybrid recommendation algorithms. In this work, we focus on the weighted hybridization and rather than using fixed weighted for the combination, we aim to propose a simple method to dynamic weight a combination of CF and CBF. Our experimental results on one of the most popular public datasets in the field of RSs - MovieLens have verified the effectiveness of our strategy against the traditional CF, and CBF approaches. It not only boost the prediction performance, but also alleviate the problem of new item cold start.
Keyword Search for Sign Language

Keyword search is the search for a written query in an archive, which is often assumed to be a collection from a spoken language. Yet, the main languages of the Deaf, i.e. sign languages, are mostly neglected in this definition due to being visual languages. In this paper, we propose a keyword search (KWS) system for a sign language. In this technique, we first extract body and hand joints from the frames of a sign language sentence and represent it with a unified spatio-temporal graph of skeleton joints. We then train our graph-convolutional-network encoder, query embedding, and selection mechanism together in a weakly supervised, end-to-end fashion. Experimental results are reported on RWTH-PHOENIX-Weather 2014T dataset.
Instance Segmentation in Remote Sensing Imagery using Deep Convolutional Neural Networks

Over the past few years, remote sensing has found applications in various fields such as hydrology, geology, glaciology, military, agriculture, etc. The advancement in satellite and sensor technology enabled users to work with large amount of high resolution data of obtained from multiple sensors. This large availability of data is efficiently exploited by employing deep neural networks in tasks ranging from image preprocessing and target recognition to high-level semantic feature extraction and object recognition. The present work involves creation of a model to perform instance segmentation in a wide variety of aerial imagery using Mask R-CNN, a state of art method for object localization, multi-class classification and segmentation. The results obtained from this work helped in further advancement of automation in analysis of data pertaining to Land Use & Land Cover (LULC) and other earth science disciplines.
Automatic Fluency Evaluation of Spontaneous Speech Using Disfluency-Based Features

This paper describes an automatic fluency evaluation of spontaneous speech. Although we regularly observe a variety of different disfluencies in spontaneous speech, we focus on two types of phenomena, i.e., filled pauses and word fragments. This paper aims to reveal that these two types of disfluencies have effects on speech fluency evaluation differently. To this end, we conduct a series of SVM classification experiments on the Japanese spontaneous speech corpus. The experimental results show that the features derived from word fragments are effective in evaluating disfluent speech especially when combined with prosodic features such as speech rate and pauses/silence, while the features from filled pauses are not effective in evaluating fluency.
OH, JEEZ! or UH-HUH? A Listener-Aware Backchannel Predictor on ASR Transcriptions

This paper presents our latest investigation on modeling backchannel in conversations. Motivated by a proactive backchanneling theory, we aim at developing a system which acts as a proactive listener by inserting backchannels, such as continuers and assessment, to influence speakers. Our model takes into account not only lexical and acoustic cues, but also introduces the simple and novel idea of using listener embeddings to mimic different backchanneling behaviours. Our experimental results on the Switchboard benchmark dataset reveal that acoustic cues are more important than lexical cues in this task and their combination with listener embeddings works best on both, manual transcriptions and automatically generated transcriptions.
AI Powered Unmanned Aerial Vehicle for Payload Transport Application

Recently unmanned aerial vehicles (UAV) have received a growing attention due to their wide range of applications. Here, we demonstrate UAVs with artificial intelligence (AI) capabilities for application in autonomous payload transport. An algorithm is developed for target detection with multiple phases on the ground, which once the target is detected, would trigger the release of the payload that is attached on the drone. The experimental results show that the average frame rate over x seconds achieved a 19.4010717352 fps (frame per second) detection speed. Releasing the payload is achieved using a 3D printed system based on rack and pinion gears. In addition, auto flight program is developed to enable the autonomous movement of the drone. As a proof-of-concept, a small drone known as "Phantom DJI" is used for .6 kg autonomous payload transport along a predefined route to a target location.
GFCN: A New Graph Convolutional Network Based on Parallel Flows

In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory. In this paper, we study the problem from a different perspective, by introducing parallel flow decomposition of graphs. The essential idea is to decompose a graph into families of non-intersecting one dimensional (1D) paths, after which, we may apply a 1D CNN along each family of paths. We demonstrate that the our method, which we call GFCN (graph flow convolutional network), is able to transfer CNN architectures to general graphs. We demonstrate effectiveness of the method with synthetic and real applications.