Reviewerly/exHarmony huggingface dataset:
Benefit of Interpolation in Nearest Neighbor Algorithms

In some studies (e.g., [C. Zhang et al. in Proceedings of the 5th International Conference on Learning Representations, OpenReview.net, 2017]) of deep learning, it is observed that overparametrized deep neural networks achieve a small testing error even when the training error is almost zero. Despite numerous works toward understanding this so-called double-descent phenomenon (e.g., [M. Belkin et al., Proc. Natl. Acad. Sci. USA, 116 (2019), pp. 15849--15854; M. Belkin, D. Hsu, and J. Xu, SIAM J. Math. Data Sci., 2 (2020), pp. 1167--1180]), in this paper, we turn to another way to enforce zero training error (without overparametrization) through a data interpolation mechanism. Specifically, we consider a class of interpolated weighting schemes in the nearest neighbors (NN) algorithms. By carefully characterizing the multiplicative constant in the statistical risk, we reveal a U-shaped performance curve for the level of data interpolation in both classification and regression setups. This sharpens the existing result [M. Belkin, A. Rakhlin, and A. B. Tsybakov, in Proceedings of Machine Learning Research 89, PMLR, 2019, pp. 1611--1619] that zero training error does not necessarily jeopardize predictive performances and claims a counterintuitive result that a mild degree of data interpolation actually strictly improves the prediction performance and statistical stability over those of the (uninterpolated) $k$-NN algorithm. In the end, the universality of our results, such as change of distance measure and corrupted testing data, will also be discussed.
A hybrid learning of multimodal cues for light field depth estimation

Learning based light field depth estimation methods have been proven to be successful, however, most of them focus on the depth cues extraction from a single representation of light field. In our opinion, existing methods are difficult to simultaneously learn various cues with different modalities, which are implied in different representations of light field. In this paper, we formulate the light field depth estimation as a pixel-wise classification task, and introduce a hybrid learning architecture to combine multimodal cues from multiple representations of light field. Three convolutional pathways are applied to predict the disparity label of each pixel from three modality representations of light field. Performing discrete disparity label classification instead of continuous disparity value regression allows us to easily fuse the prediction results of three pathways in the form of probability distribution. Evaluation experiments are carried out on the HCI 4D Light Field Benchmark. Our method, named as “FusionNet” on the benchmark website, ranks first among the published methods on high accuracy metrics of Bad Pixel Ratio 0.03 and 0.01.
Promoting active learning with mixtures of Gaussian processes

Active learning is an effective methodology to relieve the tedious and expensive work of manual annotation for many supervised learning applications. The active learning framework with good performance usually contains powerful learning models and delicate active learning strategies. Gaussian process (GP)-based active learning was proposed to be one of the most effective methods. However, the single GP suffers from the limitation of not modeling multimodal data well enough, and thus existing active learning strategies based on GPs only make use of limited information from data. In this paper, we propose three novel active learning methods, in which the existing mixture of GP model (MGP) is adjusted as the learning model and three active learning strategies are designed based on the adjusted MGP. Through experiments on multiple data sets, we analyze the performance and characteristics of the three proposed active learning methods, and further compare with popular GP-based methods and some other state-of-the-art methods.
Multi-modal page stream segmentation with convolutional neural networks

In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page documents, the preservation of document contexts is a major requirement. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into coherent multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach for PSS based on convolutional neural networks (CNN). As a first project, we combine visual information from scanned images with semantic information from OCR-ed texts for this task. The multi-modal combination of features in a single classification architecture allows for major improvements towards optimal document separation. Further to multimodality, our PSS approach profits from transfer-learning and sequential page modeling. We achieve accuracy up to 95% on multi-page documents on our in-house dataset and up to 93% on a publicly available dataset.
A Blahut-Arimoto Type Algorithm for Computing Classical-Quantum Channel Capacity

Based on Arimoto's work in 1978 [1], we propose an iterative algorithm for computing the capacity of a discrete memoryless classical-quantum channel with a finite input alphabet and a finite dimensional output, which we call the Blahut-Arimoto algorithm for classical-quantum channel, and an input cost constraint is considered. We show that to reach ε accuracy, the iteration complexity of the algorithm is up bounded by log n log ε ε where n is the size of the input alphabet. In particular, when the output state {ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> } <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x∈X</sub> is linearly independent in complex matrix space, the algorithm has a geometric convergence. We also show that the algorithm reaches an ε accurate solution with a complexity of O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> log n log ε/ε), and O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> log ε log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1-δ)</sub> D(ε/p*||p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N(0)</sup> )) in the special case, where m is the output dimension and D(p*||p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N(0)</sup> ) is the relative entropy of two distributions and δ is a positive number.
Towards Multi-modal Stress Response Modelling in Competitive League of Legends

With the constant rise in popularity of competitive video gaming (also known as Esports), Esports analytics has been a field of growing scientific interest in the recent years.Studies discussing player behaviour, skill learning and team performance have been conducted through Multiplayer Online Battle Arena games such as League of Legends.In this paper, we propose a multi-modal approach towards stress response modeling in competitive LoL games.We collect wearable physiological sensor data, mouse & keyboard logs and in-game data in order to study the relationship between player stress responses and in-game behaviour.We discuss the design criteria and propose future studies using the collected dataset.
Deep Learning Hierarchical Representation From Heterogeneous Flow-Level Communication Data

The success of a detection model depends heavily on feature engineering. Deep learning has been successfully applied in numerous research fields as a universal representation learning method. However, the heterogeneity of flow-level communication data obstructs the application of deep learning to communication representation learning, and research on this problem is still lacking. To cope with this problem, we propose a heterogeneous communication data-encoding approach to extract fixed-size encoding data to apply deep learning to heterogeneous communication data by preserving the spatiotemporal characteristics of the data. Then, we propose a feature extractor based on deep learning to automatically learn hierarchical and robust communication representations without expert knowledge. We show that the proposed approach can replicate and optimize the key steps of feature engineering well and learn hierarchical representations directly from heterogeneous communication data. Moreover, compared with features extracted with principal component analysis (PCA), manifold learning and manually crafted methods, the features extracted by deep learning are more robust and are characterized by their better adaptability to various classifiers and datasets. To the best of our knowledge, the initial work here is the first to apply deep learning techniques to heterogeneous flow-level data; consequently, the heterogeneous communication data processing method can provide technical means for the application of deep learning in other communication-related research fields.
Image Steganography and Steganalysis Based on Least Significant Bit (LSB)

The sending of encrypted messages attracts the attention of programmers and crackers who would seek to intercept and uncover the messages before arrive their intended destination. Steganography is one way to mask communication by hiding unrevealed message inside another unsuspecting message. Steganography can be combined with cryptography in order to offer a satisfactory measure of privacy and security. In this study, a new approach of the least significant bit steganography has been proposed which improves on the 1 byte least significance technique. A detail description of image steganography procedures based on Least Significant Bit (LSB) to reduce the possibility of a message being recognized is provided. It includes steganalysis - the techniques by which secret information is been extracted.
Closing the Feedback Loop Between UX Design, Software Development, Security Engineering, and Operations

There have been many evolutions of the software development lifecycle (SDLC). These differing models have moved software development groups from sequential development to a more agile and iterative development model. Increasing awareness and research focused on the cyber security landscape has resulted in a large push for "shifting security left" in the SDLC. With security engineering teams engaged earlier and more often throughout the SDLC, security issues will be found and fixed earlier, which increases efficiency while lowering cost and overhead. While this has been an important cultural and infrastructural shift for many technology companies, there is still a gap in this feedback loop that needs to be bridged: the gap between user experience designers and the software, security, and IT/operations engineers. Trade-offs have been made between security and usability---a challenge known as "usability versus security." Much of the research that propose how to change these two fields from opposing forces to being cross-functional allies offer simplified solutions but don't go into granular detail about solving the problem. This paper covers the evolution of the SDLC from the Waterfall model through the DevSecOps agile methodology and proposes a new development model: the Technology Development Lifecycle (TDLC). This TDLC model aims to keep designers, software engineers, security engineers, and IT/operations all within a tight feedback loop throughout a continuous integration/continuous development pipeline. We will discuss various workflows, use cases, and technologies that can be used later on to implement a working environment that can enforce the TDLC model.
Multi-Behavior Fusion Based Potential Field Method for Path Planning of Unmanned Surface Vessel

The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
Permutation trinomials over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

We consider four classes of polynomials over the fields Fq3, q=ph, p>3, f1(x)=xq2+q−1+Axq2−q+1+Bx, f2(x)=xq2+q−1+Axq3−q2+q+Bx, f3(x)=xq2+q−1+Axq2−Bx, f4(x)=xq2+q−1+Axq−Bx, where A,B∈Fq. We find sufficient conditions on the pairs (A,B) for which these polynomials permute Fq3 and we give lower bounds on the number of such pairs.
Multipath Based Adaptive Concurrent Transfer for Real-Time Video Streaming Over 5G Multi-RAT Systems

5G systems use multiple radio access technology (multi-RAT) to provide broadband, low latency, and high reliability services.Multi-RAT is effective when multipath transmission is used over multichannels.This is why multipath based transmission is one of the key technologies that can improve the throughput and reliability of real-time video streaming over 5G networks.This paper proposes two multipath based concurrent transfer techniques; fast concurrent transfer (FCT) and reliable concurrent transfer (RCT).FCT aims to minimize the round trip time (RTT) required for frame transmission by arranging each of the packets constituting a frame to be sent over multiple channels.In an environment where there are multiple channels, RCT performs redundant transmission of packets, so that even if a packet is lost in one path, the lost packet can be received safely over another path.FCT aims at securing the reliability of data transmission.In addition, a multipath based adaptive concurrent transmission (MACT) scheme is proposed which controls the ratio of FCT and RCT adaptively considering the state of the receiver buffer, inflow throughput, and decoding rate to enhance the reliability and throughput performance.The simulation results show that the proposed MACT scheme results in a better performance compared to the conventional transmission schemes in various network channel conditions.
Selecting Features Subsets Based on Support Vector Machine-Recursive Features Elimination and One Dimensional-Naïve Bayes Classifier using Support Vector Machines for Classification of Prostate and Breast Cancer

Deaths caused by cancer are expected to continue to increase, especially for prostate cancer and breast cancer. Both diseases are the most common types of cancer for men and women in the world. The number of deaths can be reduced by the early detection of using machine learning. One of them is the classification of prostate cancer and breast cancer data. Cancer data used has a variety of features, but not all features are essential features. In this study, we used Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and One-Dimensional Naïve Bayes Classifier (1-DBC) as feature selection methods. In both methods, it will get a ranking for each feature. The use of these two methods in the classification of prostate cancer and breast cancer data results in a high level of evaluation. Both of these methods can produce an accuracy rate of 95.61%, the precision of 100%, and recall of 93.61%. In additional evaluation, SVM-RFE has lower running time than 1-DBC.
Quantum-Inspired Neural Architecture Search

Deep neural networks have gained attention in the last decade as significant progress has been made in a variety of tasks thanks to these new architectures. Most of the time, hand-designed networks are responsible for this incredible success. However, this engineering process demands considerable time and expert knowledge, which leads to an increasing interest in automating the design of deep architectures. Several new algorithms have been proposed to address the neural architecture search problem, but many of them require significant computational resources. Quantum-inspired evolutionary algorithms (QIEA) have their roots on quantum computing principles and present promising results in respect to faster convergence. In this work, we propose Q-NAS (Quantum-inspired Neural Architecture Search): a quantum-inspired algorithm to search for deep neural architectures by assembling substructures and optimizing some numerical hyperparameters. We present the first results applying Q-NAS on the CIFAR-10 dataset using only 20 K80 GPUs for about 50 hours. The obtained networks are relatively small (less than 20 layers) compared to other state-of-the-art models and achieve promising accuracies with considerably less computational cost than other NAS algorithms.
Graph Convolutional Networks with Structural Attention Model for Aspect Based Sentiment Analysis

With the amount of user-generated information on the Web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Aspect based sentiment analysis has gained increasing attention in decade years, but it remains a daunting task. Recently, approaches based on recurrent neural networks and convolutional neural networks have shown competitive results in this field. However, they don't take fully account of the entire text structure and the relation between words in a given document. In this paper, we propose a novel neural network method to address this problem, in which the text is treated as a graph and the aspect is the specific area of the graph. For the first time, we apply graph convolutional neural networks and structural attention model to aspect based sentiment analysis. Experiments on public-available datasets demonstrate the efficiency and effectiveness of our model.
Improving the Accuracy of Vulnerability Report Classification Using Term Frequency-Inverse Gravity Moment

Software vulnerability analysis is one of the critical issues in the software industry, and vulnerability classification plays a major role in this analysis. A typical vulnerability classification model usually involves a stage of term selection, in which the relevant terms are identified via feature selection. It also involves a stage of term weighting, in which document weights for the selected terms are computed, and a stage for classifier learning. Generally, the term frequency-inverse document frequency (TF-IDF) is the most widely used term-weighting method. However, empirical evidence shows that the TF-IDF is plagued with issues pertaining to its effectiveness. This paper introduces a new approach for vulnerability classification, which is based on term frequency and inverse gravity moment (TF-IGM). The proposed method is validated by empirical experiments using three machine learning algorithms on ten publicly available vulnerability datasets. The result shows that TF-IGM outperforms the benchmark method across the applications studied.
Challenges of Implementing Training and Awareness Programs Targeting Cyber Security Social Engineering

Information security is one of the growing sources of concern that organizations are dealing with today. With increased levels of sophistication of social engineering threats, the exploits from such attacks are evolving. This study highlights some of the challenges that organizations encounter in the process of developing the human knowledge to fight against social engineering attacks. Despite state-of-the-art cyber security preparations and trained personnel, hackers are still successful in their malicious acts of stealing sensitive information that is crucial to organizations. This study further discusses the need for human resource departments to impose training requirements for new hires as part of onboarding processes. The factors influencing users' proficiency in the process of threat detection and mitigation have been identified as business environmental, social, political, constitutional, organizational, economical, and personal. Challenges with respect to both traditional and modern tools have been analyzed to suggest the need for profiling at-risk employees and developing training programs at each level of the hierarchy to ensure that the hackers do not succeed.
Development of a Distributed Blockchain eVoting System

Over the years, elections at different levels are the subject of many disputes, consolidation and reaffirmation of confidence in different leaders. These elections range from either boardroom elections, local elections to national elections. Correspondingly, electioneering as an institutional process has transformed with both technological advancements as well political sophistication. Blockchain as a nascent technology has ushered in a range of opportunities in different sectors such as finance, real estate, supply chain management, medical records, elections etc. In this paper, we leveraged on some of the key characteristics of Blockchain technology in form of immutability, auditability, confidentiality, transparency, decentralization etc to develop a Blockchain eVoting network system that can be utilized for geographically dispersed elections. Key users of the system or actors were isolated and their interactions with the system identified using Use Case Methodology. The system actors are the voters, miners that consists of Universities and public libraries, central authority, candidates and the voters. The different stages of an election process such as pre-election, registration of both candidates and voters, voting/balloting, tallying and auditing were equally enumerated. The interactions of the actors with the system were equally captured, analyzed and used to develop the conceptual system. The proposed system is made up of a multichain Blockchain network, an arbitration server, distributed database, an interactive, multi-device graphic user interface GUI, and an application server. To engender anonymity, a combination of Digital Signature and Secure Hash Algorithm SHA was proposed, also Proof of Work PoW was the proposed consensus algorithm. The identified stages of the election were evaluated against the requirements of an eVoting system. The proposed system was found to satisfy the requirements of an eVoting system such as transparency, privacy, scalability, receipt freeness, security, integrity, accuracy, auditable etc.
Blockchain Based Anonymous Voting System Using zkSNARKs

This paper presents a very simple approach for a fair electronic voting system that guarantees anonymity, coercion resistance, correctness, easy tallying, eligibility, fairness, high availability, integrity, robustness, voter authentication, voter confidentiality, vote verifiability and public verifiability of any electoral process. These properties are achieved using blockchain technology. A relatively new type of zero knowledge proof known as zkSNARKs has been used to provide voter unlinkability.