Reviewerly/exHarmony huggingface dataset:
Few-Shot Model-Based Adaptation in Noisy Conditions

Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection. In physical systems, additional challenge may be posed by domain noise, which is present in virtually all real-world applications. In this letter, we propose to perform few-shot adaptation of dynamics models in noisy conditions using an uncertainty-aware Kalman filter-based neural network architecture. We show that the proposed method, which explicitly addresses domain noise, improves few-shot adaptation error over a blackbox adaptation LSTM baseline, and over a model-free on-policy reinforcement learning approach, which tries to learn an adaptable and informative policy at the same time. The proposed method also allows for system analysis by analyzing hidden states of the model during and after adaptation.
Digital Warfare Against COVID-19: Global Use of Contact-Tracing Apps

The current COVID-19 (coronavirus disease 2019) pandemic has perpetuated a worldwide tussle to control this infection and minimizing death toll and economic damages. The year 2020 would always be remembered as a year of war against a virus, but it would also be marked as a year showing upsurge of technology evolution to deal with infections. Contact tracing has emerged as a new buzzword. Contact tracing digital tools have been introduced worldwide to prevent transmission. In this article, we have outlined various contact tracing apps being used globally, which are rated according to their safety, privacy, and efficacy.
OSUMI: On-Shelf Utility Mining from Itemset-based Data

As an important technique for dealing with transactional database in the field of data mining, high-utility itemset mining (HUIM) can be used to discover itemsets which have a high utility. However, it has a bias when towarding the item combinations which have more exhibition period since they have more opportunity to generate a high utility. To address this, the on-shelf time period of items need to be considered, thus on-shelf utility mining (OSUM) can be applied in the application which is more closer to the actual situation. Currently several models have been proposed to deal with the OSUM problem, but they still suffer from the requirement that it needs to maintain a massive candidates in memory and to scan database many times. In this paper, we propose an effective algorithm named OSUMI (On-Shelf Utility Mining from Itemset-based data) which can discover the on-shelf itemsets with high utility in a more practical way. More precisely, in order to avoid the problems of high memory consumption, OSUMI applies some properties of on-shelf utility. Besides, two upper-bounds named subtree utility and local utility are applied to prune the search space. Finally, an extensive experimental study on two real on-shelf datasets shows that our proposed algorithm can be significantly faster than the state-of-the-art algorithm for this mining task.
Off-line signature verification using elementary combinations of directional codes from boundary pixels

Abstract Verifying the genuineness of official documents, such as bank checks, certificates, contract forms, bonds, etc., remains a challenging task when it comes to accuracy and robustness. Here, the genuineness is related to the degree of match of the signature contained in the documents relating to the original signatures of the authorized person. Signatures of authorized persons are considered known in advance. In this paper, a novel feature set is introduced based on quasi-straightness of boundary pixel runs for signature verification. We extract the quasi-straight line segments using elementary combinations of the directional codes from the signature boundary pixels and subsequently we obtain the feature set from various quasi-straight line classes. The quasi-straight line segments provide a blending of straightness and small curvatures resulting in a robust feature set for the verification of signatures. We have used Support Vector Machine (SVM) for classification and have shown results on standard signature datasets like CEDAR (Center of Excellence for Document Analysis and Recognition) and GPDS-100 (Grupo de Procesado Digital de la Senal). The results establish how the proposed method outperforms the existing state of the art.
Cross-lingual Speaker Verification: Evaluation on X-Vector Method

Automatic Speaker Verification (ASV) systems accuracy is based on the spoken language used in training and enrolling speakers. Language dependency makes voice-based security systems less robust and generalizable to a wide range of applications. In this work, a study on language dependency of a speaker verification system and experiments are performed to benchmark the robustness of the x-vector based techniques to language dependency. Experiments are carried out on a smartphone multi-lingual dataset with 50 subjects containing utterances in four different languages captured in five sessions. We have used two world training datasets, one with only one language and one with multiple languages. Results show that performance is degraded when there is a language mismatch in enrolling and testing. Further, our experimental results indicate that the performance degradation depends on the language present in the word training data.
Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network

Fine-grained sentiment analysis of social platforms like Twitter and Facebook nowadays becomes increasingly important, as it can reflect public opinions towards target entities such as politicians. Entity-Level Sentiment Analysis (ELSA) is an important fine-grained SA task, aiming to identify the sentiment over each entity mentioned in a sentence. Most previous methods to this task primarily rely on the text, but ignoring the other useful multimodal data sources (e.g., images). Therefore, in this paper, we aim to explore the usefulness of associated images for ELSA in multimodal tweets (especially political tweets). Specifically, we propose an Entity-Aware Multimodal Network (EAMN), and apply it to political tweets for understanding public opinions towards some politicians. Experiment results show that the associated images are generally useful for ELSA, and our EAMN model achieves the state-of-the-art results on two public Twitter datasets and our political Twitter datasets.
Variational Bayesian based adaptive PDA filter in scenarios with unknown detection probability and heavy-tailed process noise

For target tracking systems, the probability of detecting a target is difficult to determine, and the process noise often has non-Gaussian heavy-tailed characteristics owing to interference from outliers. To address the issues associated with single target tracking within clutters in scenarios with an unknown detection probability and heavy-tailed process noise, this paper presents a variational Bayesian-based adaptive probabilistic data association filter (VB-APDAF). The beta distribution, Pearson type VII distribution and multinomial distribution are used to model the detection probability, the process noise, and the association events, respectively. To guarantee the conjugation, a novel parameter estimation strategy is employed. In this strategy, the previous state is introduced in the state update process to construct the joint probability density function of parameters to be estimated and data set. The VB framework is used to estimate the target state, detection probability, and associated events. An experiment was performed under simulated conditions to demonstrate the effectiveness of the proposed filter.
Quantum Cryptography Research: A Scientometric Assessment of Global Publications during 1992-2019

The study examines global “Quantum Cryptography” research on a set of quantitative and qualitative metrics to understand the status of research in the subject at global, national, institutional, and individual level. The analysis is based on publications data in the subject comprising a total of 10801 publications as indexed in the Scopus database covering the period 1992–2020. The study provides a window to most productive countries, research organizations and authors in the subject, to most popular broad subject areas in research, to top modes of research communication, and in addition it describes the characteristics of highly cited papers. The study maps the most productive countries, organizations, authors, and keywords in the context of collaborative research. The quantum cryptography research registered a 24.76% annual average growth, accrued an average of 23.01 citations per paper (CPP) since their publication, and it contributed a 4.03% of its output as highly cited papers, with an average of 305.46 citations per paper since their publication. Scholarly journals accounted for a 59.61% global publications share. Nearly 29% of the total output on “Quantum Cryptography” resulted from projects funded by 100+ national and international funding agencies.
A Novel 3D-Unet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation

The task of predicting smooth and edge-consistent depth maps is notoriously difficult for single image depth estimation. This paper proposes a novel Bilateral Grid based 3D convolutional neural network, dubbed as 3DBG-UNet, that parameterize high dimensional feature space by encoding compact 3D bilateral grids with UNets and infers sharp geometric layout of the scene. Further, an another novel 3DBGES-UNet model is introduced that integrate 3DBG-UNet for inferring an accurate depth map given a single color view. The 3DBGES-UNet concatenate 3DBG-UNet geometry map with the inception network edge accentuation map and a spatial object's boundary map obtained by leveraging semantic segmentation and train the UNet model with ResNet backbone. Both models are designed with a particular attention to explicitly account for edges or minute details. Preserving sharp discontinuities at depth edges is critical for many applications such as realistic integration of virtual objects in AR video or occlusion-aware view synthesis for 3D display applications. The proposed depth prediction network achieves state-of-the-art performance in both qualitative and quantitative evaluations on the challenging NYUv2-Depth data. The code and corresponding pre-trained weights will be made publicly available.
Eye-To-Eye: Towards Visualizing Eye Gaze Data

The work in this paper introduces a platform that is able to visualize eye gaze data. The platform introduces two different directions or algorithms. The first is for videos with heat-map to convert the heat map data into raw numbers. The algorithm takes the video containing the heat map and cuts it into frames. In each frame, it locates the position of the heat-map by searching for the specific colour values and then displaying the position of heat-map in each frame. The algorithm also calculates the fixation points and how long each fixation was for the entirety of the video. The second algorithm is to do the opposite of the first and that is by converting the raw data of eye-movement to a heat map. We are also providing visualization platform for eye-tracking data.
Feature Importance Evaluation of Smartphone Touch Gestures for Biometric Authentication

In this work, we present a method of feature selection for smartphone touch gesture classification. Touch gestures, also known as touchscreen features are used as behavioral attributes with machine learning classifiers to implement authentication systems for smartphones. We propose to use a publically available dataset and perform a feature scoring with the extreme gradient boosting (XGBoost) algorithm to select the most relevant features. We carried out two experiments: in the first one, we used a vector of 30 features for the classification and we performed feature ranking. In the second experiment, we used a subset of 7 features based on the ranking given by the XGBoost algorithm. Classification results are evaluated with the state of the art approaches. We achieved an accuracy of 99.41% using only a feature vector of 7 variables, this demonstrates that touchscreen features contain relevant information about the human identity and could be used for biometric authentication.
Cryo-CMOS Interfaces for Large-Scale Quantum Computers

Cryogenic CMOS (cryo-CMOS) is a viable technology for the control interface of the large-scale quantum computers able to address non-trivial problems. In this paper, we demonstrate state-of-the-art cryo-CMOS circuits and systems for such application and we discuss the challenges still to be faced on the path towards practical quantum computers.
Beyond Cache Attacks

System-on-Chips (SoCs) are a key enabling technology for the Internet-of-Things (IoT), a hyper-connected world where on- and inter-chip communication is ubiquitous. SoCs usually integrate cryptographic hardware cores for confidentiality and authentication services. However, these components are prone to implementation attacks. During the operation of a cryptographic core, the secret key may passively be inferred through cache observations. Access-driven attacks exploiting these observations are therefore a vital threat to SoCs operating in IoT environments. Previous works have shown the feasibility of these attacks in the SoC context. Yet, the SoC communication structure can be used to further improve access-based cache attacks. The communication attacks are not as well-understood as other micro-architectural attacks. It is important to raise the awareness of SoC designers of such a threat. To this end, we present four contributions. First, we demonstrate an improved Prime+Probe attack on four different AES-128 implementations (original transformation tables, T 0 -Only, T 2KB , and S-Box). As a novelty, this attack exploits the collisions of the bus-based SoC communication to further increase its efficiency. Second, we explore the impact of preloading on the efficiency of our communication-optimized attack. Third, we integrate three countermeasures ( shuffling , mini-tables , and Time-Division Multiple Access (TDMA) bus arbitration ) and evaluate their impact on the attack. Although shuffling and mini-tables countermeasures were proposed in previous work, their application as countermeasures against the bus-based attack was not studied before. In addition, TDMA as a countermeasure for bus-based attacks is an original contribution of this work. Fourth, we further discuss the implications of our work in the SoC design and its perspective with the new cryptographic primitives proposed in the ongoing National Institute of Standard and Technology Lightweight Cryptography competition. The results show that our improved communication-optimized attack is efficient, speeding up full key recovery by up to 400 times when compared to the traditional Prime+Probe technique. Moreover, the protection techniques are feasible and effectively mitigate the proposed improved attack.
Efficient Execution of Graph Algorithms on CPU with SIMD Extensions

Existing state-of-the-art CPU graph frameworks take advantage of multiple cores, but not the SIMD capability within each core. In this work, we retarget an existing GPU graph algorithm compiler to obtain the first graph framework that uses SIMD extensions on CPUs to efficiently execute graph algorithms. We evaluate this compiler on 10 benchmarks and 3 graphs on 3 different CPUs and also compare to the GPU. Evaluation results show that on a 8-core machine, enabling SIMD on a naive multi-core implementation achieves an additional 7.48x speedup, averaged across 10 benchmarks and 3 inputs. Applying our SIMD-targeted optimizations improves the plain SIMD implementation by 1.67x, outperforming a serial implementation by 12.46x. On average, the optimized multi-core SIMD version also outperforms the state-of-the-art graph framework, GraphIt, by 1.53x, averaged across 5 (common) benchmarks. SIMD execution on CPUs closes the gap between the CPU and GPU to 1.76x, but the CPU virtual memory performs better when graphs are much bigger than available physical memory.
MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels

Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. In each iteration, before conventional training, the meta-training loop updates soft-labels so that resulting gradients updates on the base classifier would yield minimum loss on meta-data. Soft-labels are generated from extracted features of data instances, and the mapping function is learned by a single layer perceptron (SLP) network, which is called MetaLabelNet. Following, base classifier is trained by using these generated soft-labels. These iterations are repeated for each batch of training data. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases. We perform extensive experiments on benchmark datasets with both synthetic and real-world noises. Results show that our approach outperforms existing baselines. The source code of the proposed model is available at https://github.com/gorkemalgan/MetaLabelNet.
Energy-aware very fast decision tree

Abstract Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.
WITHDRAWN: Data clustering using hybrid of feature linkage weight based feature reduction and particle Swarm optimization

The paper proposes a novel Feature Linkage Weight with PSO (FLWPSO) by combining the feature linkage based weight reduction along with Particle Swarm Optimization to build a novel cluster, called as Feature Linkage Weight with Particle Swarm Optimization (FLWPSO). The proposed clustering method is best suited for identifying the centroids in a user defined number of clusters. The fitness function is proposed in a novel manner which can significantly improve the PSO oriented clustering of data. The overall quality of the cluster is improved using the FLW. The results shows high accuracy and ARI ratio as that of other state of art clustering methods.
Proof of X: Experimental Insights on Blockchain Consensus Algorithms in Energy Markets

The current centralized model of the electricity market is not efficient in performing distributed energy transactions required for the transactive smart grid. One of the prominent solutions to this issue is to integrate blockchain technologies, which promise transparent, tamper-proof, and secure transaction systems specifically suitable for the decentralized and distributed energy markets. Blockchain has already been shown to successfully operate in a microgrid peer-to-peer (P2P) energy market. The prime determinant of different blockchain implementations is the consensus algorithm they use to reach consensus on which blocks/transactions to accept as valid in a distributed environment. Although different blockchain implementations have been proposed independently for P2P energy market in the microgrid, quantitative experimental analyses and comparison of the consensus algorithms that the different blockchains may use for energy markets, has not been studied. Identifying the right consensus algorithm to use is essential for scalability and operation of the energy market. To this end, we evaluate three popular consensus algorithms: (i) proof of work (PoW), (ii) proof of authority (PoA), and (iii) Istanbul Byzantine fault tolerance (IBFT), running them on a network of nodes set up using a network of docker nodes to form a microgrid energy market. Using a series of double auctions, we assess each algorithm's viability using different metrics, such as time to reach consensus and scalability. The results indicate that PoA is the most efficient and scalable consensus algorithm to hold double auctions in the smart grid. We also identified the minimum hardware specification necessary for devices such as smart meters, which may run these consensus algorithms.
How it Flies and Why it Flies? Volleyball Trajectory Segmentation and Classification

Analyzing volleyball videos based on 3D ball trajectories was relatively overlooked before. In this brief, we focus on how ball trajectories can benefit volleyball video analysis. Based on videos captured by two cameras from different viewpoints, we detect the volleyball and construct 3D ball trajectories. We then propose a trajectory segmentation and classification method based on BERT (Bidirectional Encoder Representation for Transformer). The volleyball at each frame can be categorized into one of six trajectory classes, e.g., serve and attack, and a long ball trajectory showing the ball being hit back and forth is appropriately segmented. We believe that this is a very first study adopting the language model technique to analyze ball trajectories, and results of trajectory segmentation and classification can enable more advanced volleyball analysis.