OpenRaiser/Intern-Atlas huggingface dataset:
Emulation of physical processes with Emukit
Venue: arXiv cs.LG 2021
Year: 2021

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has developed a number of methods to facilitate decision making, but so far they are scattered in multiple different toolkits, and generally rely on a fixed backend. In this paper, we present Emukit, a highly adaptable Python toolkit for enriching decision making under uncertainty. Emukit allows users to: (i) use state of the art methods including Bayesian optimization, multi-fidelity emulation, experimental design, Bayesian quadrature and sensitivity analysis; (ii) easily prototype new decision making methods for new problems. Emukit is agnostic to the underlying modeling framework and enables users to use their own custom models. We show how Emukit can be used on three exemplary case studies.
Exploring eFPGA-based Redaction for IP Protection
Venue: arXiv cs.CR 2021
Year: 2021

Recently, eFPGA-based redaction has been proposed as a promising solution for hiding parts of a digital design from untrusted entities, where legitimate end-users can restore functionality by loading the withheld bitstream after fabrication. However, when deciding which parts of a design to redact, there are a number of practical issues that designers need to consider, including area and timing overheads, as well as security factors. Adapting an open-source FPGA fabric generation flow, we perform a case study to explore the trade-offs when redacting different modules of open-source intellectual property blocks (IPs) and explore how different parts of an eFPGA contribute to the security. We provide new insights into the feasibility and challenges of using eFPGA-based redaction as a security solution.
EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization
Venue: arXiv cs.LG 2021
Year: 2021

Trust Region Policy Optimization (TRPO) is a popular and empirically successful policy search algorithm in reinforcement learning (RL). It iteratively solved the surrogate problem which restricts consecutive policies to be close to each other. TRPO is an on-policy algorithm. On-policy methods bring many benefits, like the ability to gauge each resulting policy. However, they typically discard all the knowledge about the policies which existed before. In this work, we use a replay buffer to borrow from the off-policy learning setting to TRPO. Entropy regularization is usually used to improve policy optimization in reinforcement learning. It is thought to aid exploration and generalization by encouraging more random policy choices. We add an Entropy regularization term to advantage over π, accumulated over time steps, in TRPO. We call this update EnTRPO. Our experiments demonstrate EnTRPO achieves better performance for controlling a Cart-Pole system compared with the original TRPO
Task-Specific Dependency-based Word Embedding Methods
Venue: arXiv cs.CL 2021
Year: 2021

Two task-specific dependency-based word embedding methods are proposed for text classification in this work. In contrast with universal word embedding methods that work for generic tasks, we design task-specific word embedding methods to offer better performance in a specific task. Our methods follow the PPMI matrix factorization framework and derive word contexts from the dependency parse tree. The first one, called the dependency-based word embedding (DWE), chooses keywords and neighbor words of a target word in the dependency parse tree as contexts to build the word-context matrix. The second method, named class-enhanced dependency-based word embedding (CEDWE), learns from word-context as well as word-class co-occurrence statistics. DWE and CEDWE are evaluated on popular text classification datasets to demonstrate their effectiveness. It is shown by experimental results they outperform several state-of-the-art word embedding methods.
VLSI Implementation of Cryptographic Algorithms & Techniques: A Literature Review
Venue: arXiv cs.CR 2021
Year: 2021

Through the years, the flow of Data and its transmission have increased tremendously and so has the security issues to it. Cryptography in recent years with the advancement of VLSI has led to its implementation of Encryption and Decryption techniques, where the process of translating and converting plaintext into cypher text and vice versa is made possible. In this paper, the review of various aspects of VLSI's implementation of encryption and decryption are covered. To systemize the material, the information about topics such as Private Key Encryption, Index Technique, Blowfish Algorithm, DNA cryptography, and many more are reviewed. Ultimately, with this review, the basic understanding of different VLSI techniques of Encryption and Decryption can be studied and implemented.
Comparison of Indicators of Location Homophily Using Twitter Follow Graph
Venue: arXiv cs.SI 2021
Year: 2021

Location homophily is a tendency of Twitter users whose followers tend to be in the same or nearby areas. Intuitively, although users with a higher number of follower relationships might have negative homophily indicators, it is worth consulting actual Twitter data. Moreover, there may be certain functions regarding the numbers of friends and followers that are more directly correlated to the homophily. In this study, the ratio of the number of friends to the number of followers is shown to be a more effective negative indicator of homophily, and the results for 10 different countries are verified.
Semantic Host-free Trojan Attack
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a novel host-free Trojan attack with triggers that are fixed in the semantic space but not necessarily in the pixel space. In contrast to existing Trojan attacks which use clean input images as hosts to carry small, meaningless trigger patterns, our attack considers triggers as full-sized images belonging to a semantically meaningful object class. Since in our attack, the backdoored classifier is encouraged to memorize the abstract semantics of the trigger images than any specific fixed pattern, it can be later triggered by semantically similar but different looking images. This makes our attack more practical to be applied in the real-world and harder to defend against. Extensive experimental results demonstrate that with only a small number of Trojan patterns for training, our attack can generalize well to new patterns of the same Trojan class and can bypass state-of-the-art defense methods.
Wavelet: Code-based postquantum signatures with fast verification on microcontrollers
Venue: arXiv cs.CR 2021
Year: 2021

This work presents the first full implementation of Wave, a postquantum code-based signature scheme. We define Wavelet, a concrete Wave scheme at the 128-bit classical security level (or NIST postquantum security Level 1) equipped with a fast verification algorithm targeting embedded devices. Wavelet offers 930-byte signatures, with a public key of 3161 kB. We include implementation details using AVX instructions, and on ARM Cortex-M4, including a solution to deal with Wavelet's large public keys, which do not fit in the SRAM of a typical embedded device. Our verification algorithm is $\approx 4.65 \times$ faster then the original, and verifies in 1 087 538 cycles using AVX instructions, or 13 172 ticks in an ARM Cortex-M4.
Tensor Network Kalman Filtering for Large-Scale LS-SVMs
Venue: arXiv cs.LG 2021
Year: 2021

Least squares support vector machines are a commonly used supervised learning method for nonlinear regression and classification. They can be implemented in either their primal or dual form. The latter requires solving a linear system, which can be advantageous as an explicit mapping of the data to a possibly infinite-dimensional feature space is avoided. However, for large-scale applications, current low-rank approximation methods can perform inadequately. For example, current methods are probabilistic due to their sampling procedures, and/or suffer from a poor trade-off between the ranks and approximation power. In this paper, a recursive Bayesian filtering framework based on tensor networks and the Kalman filter is presented to alleviate the demanding memory and computational complexities associated with solving large-scale dual problems. The proposed method is iterative, does not require explicit storage of the kernel matrix, and allows the formulation of early stopping conditions. Additionally, the framework yields confidence estimates of obtained models, unlike alternative methods. The performance is tested on two regression and three classification experiments, and compared to the Nyström and fixed size LS-SVM methods. Results show that our method can achieve high performance and is particularly useful when alternative methods are computationally infeasible due to a slowly decaying kernel matrix spectrum.
Time Complexity Analysis of an Evolutionary Algorithm for approximating Nash Equilibriums
Venue: arXiv cs.GT 2021
Year: 2021

The framework outlined in [arXiv:2010.13024] provides an approximation algorithm for computing Nash equilibria of normal form games. Since NASH is a well-known PPAD-complete problem, this framework has potential applications to other $PPAD$ problems. The correctness of this framework has been empirically validated on 4 well-studied 2x2 games: Prisoner's Dilemma, Stag Hunt, Battle, and Chicken. In this paper, we provide the asymptotic time-complexities for these methods and in particular, verify that for 2x2 games the worst-case complexity is linear in the number of actions an agent can choose from.
Two Decades of Game Jams
Venue: arXiv cs.HC 2021
Year: 2021

In less than a year's time, March 2022 will mark the twentieth anniversary of the first documented game jam, the Indie Game Jam, which took place in Oakland, California in 2002. Initially, game jams were widely seen as frivolous activities. Since then, they have taken the world by storm. Game jams have not only become part of the day-to-day process of many game developers, but jams are also used for activist purposes, for learning and teaching, as part of the experience economy, for making commercial prototypes that gamers can vote on, and more. Beyond only surveying game jams and the relevant published scientific literature from the last two decades, this paper has several additional contributions. It builds a history of game jams, and proposes two different taxonomies of game jams - a historical and a categorical. In addition, it discusses the definition of game jam and identifies the most active research areas within the game jam community such as the interplay and development with local communities, the study and analysis of game jammers and organisers, and works that bring a critical look on game jams.
Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
Venue: arXiv cs.NE 2021
Year: 2021

Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction with TGP: crossover and insertion. In this paper, we shall focus on how to apply TGP for solving multi-objective optimization problems which are quite unusual for GP. Each TGP individual stores the output of a computer program (tree) representing a point in the search space. Numerical experiments show that TGP is able to solve very fast and very well the considered test problems.
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling
Venue: arXiv cs.CL 2021
Year: 2021

Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems. These two tasks are closely-related and can flourish each other. Since only a few utterances can be utilized for identifying fast-emerging new intents and slots, data scarcity issue often occurs when implementing IC and SF. However, few IC/SF models perform well when the number of training samples per class is quite small. In this paper, we propose a novel explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. Its highlights are as follows. (i) The model extracts intent and slot representations via bidirectional interactions, and extends prototypical network to achieve explicit-joint learning, which guarantees that IC and SF tasks can mutually reinforce each other. (ii) The model integrates with supervised contrastive learning, which ensures that samples from same class are pulled together and samples from different classes are pushed apart. In addition, the model follows a not common but practical way to construct the episode, which gets rid of the traditional setting with fixed way and shot, and allows for unbalanced datasets. Extensive experiments on three public datasets show that our model can achieve promising performance.
Towards a Theory of Domains for Harmonic Functions and its Symbolic Counterpart
Venue: arXiv cs.SC 2021
Year: 2021

In this paper, we begin by reviewing the calculus induced by the framework of [10]. In there, we extended Polylogarithm functions over a subalgebra of noncommutative rational power series, recognizable by finite state (multiplicity) automata over the alphabet X = {x 0 , x 1 }. The stability of this calculus under shuffle products relies on the nuclearity of the target space [31]. We also concentrated on algebraic and analytic aspects of this extension allowing to index polylogarithms, at non positive multi-indices, by rational series and also allowing to regularize divergent polyzetas, at non positive multi-indices [10]. As a continuation of works in [10] and in order to understand the bridge between the extension of this "polylogarithmic calculus" and the world of harmonic sums, we propose a local theory, adapted to a full calculus on indices of Harmonic Sums based on the Taylor expansions, around zero, of polylogarithms with index x 1 on the rightmost end. This theory is not only compatible with Stuffle products but also with the Analytic Model. In this respect, it provides a stable and fully algorithmic model for Harmonic calculus. Examples by computer are also provided 6 .
Assessing Evaluation Metrics for Speech-to-Speech Translation
Venue: arXiv cs.CL 2021
Year: 2021

Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate speech-to-speech translation is an open question which has not previously been explored. Translating to speech rather than to text is often motivated by unwritten languages or languages without standardized orthographies. However, we show that the previously used automatic metric for this task is best equipped for standardized high-resource languages only. In this work, we first evaluate current metrics for speech-to-speech translation, and second assess how translation to dialectal variants rather than to standardized languages impacts various evaluation methods.
Software Engineering Meets Systems Engineering: Conceptual Modeling Applied to Engineering Operations
Venue: arXiv cs.SE 2021
Year: 2021

Models are fundamentally crucial to many scientific fields, including software engineering, systems engineering, enterprise modeling, and business modeling. This paper focuses on diagrammatic conceptual modeling, as opposed to mathematical or computational models, wherein a conceptual model is a translation of reality processes into an abstract mechanism that has similar structure and parallel events of the external processes. Although various modeling approaches exist, including UML (Unified Modeling Language) in software engineering and its dialect, SysML (System Modeling Language), in systems engineering, several difficulties arise in such models, including the problem of model multiplicity that is related to the lack an integrated view of structure and behavior. This paper generalizes conceptual modeling to be applied in organizations at large. According to authorities, the so-called organization theory portrays organizations as machine-like systems. As a machine, an organization coordinates its parts to transform inputs into outputs. Therefore, we synthesize the notion of an organization as a machine and apply a new modeling methodology called thinging machine (TM) to real engineering operations. The results show the viability of the TM methodology serving as a foundation for high-level modelling of systems.
Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges
Venue: arXiv cs.DC 2021
Year: 2021

In this survey, we discuss the challenges of executing scientific workflows as well as existing Machine Learning (ML) techniques to alleviate those challenges. We provide the context and motivation for applying ML to each step of the execution of these workflows. Furthermore, we provide recommendations on how to extend ML techniques to unresolved challenges in the execution of scientific workflows. Moreover, we discuss the possibility of using ML techniques for in-situ operations. We explore the challenges of in-situ workflows and provide suggestions for improving the performance of their execution using ML techniques.
MisConv: Convolutional Neural Networks for Missing Data
Venue: arXiv cs.LG 2021
Year: 2021

Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots. While imputation-based techniques are still one of the most popular solutions, they frequently introduce unreliable information to the data and do not take into account the uncertainty of estimation, which may be destructive for a machine learning model. In this paper, we present MisConv, a general mechanism, for adapting various CNN architectures to process incomplete images. By modeling the distribution of missing values by the Mixture of Factor Analyzers, we cover the spectrum of possible replacements and find an analytical formula for the expected value of convolution operator applied to the incomplete image. The whole framework is realized by matrix operations, which makes MisConv extremely efficient in practice. Experiments performed on various image processing tasks demonstrate that MisConv achieves superior or comparable performance to the state-of-the-art methods.
Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity
Venue: arXiv cs.LG 2021
Year: 2021

We extend the adversarial/non-stochastic multi-play multi-armed bandit (MPMAB) to the case where the number of arms to play is variable. The work is motivated by the fact that the resources allocated to scan different critical locations in an interconnected transportation system change dynamically over time and depending on the environment. By modeling the malicious hacker and the intrusion monitoring system as the attacker and the defender, respectively, we formulate the problem for the two players as a sequential pursuit-evasion game. We derive the condition under which a Nash equilibrium of the strategic game exists. For the defender side, we provide an exponential-weighted based algorithm with sublinear pseudo-regret. We further extend our model to heterogeneous rewards for both players, and obtain lower and upper bounds on the average reward for the attacker. We provide numerical experiments to demonstrate the effectiveness of a variable-arm play.
Mining frequency-based sequential trajectory co-clusters
Venue: arXiv cs.LG 2021
Year: 2021

Co-clustering is a specific type of clustering that addresses the problem of finding groups of objects without necessarily considering all attributes. This technique has shown to have more consistent results in high-dimensional sparse data than traditional clustering. In trajectory co-clustering, the methods found in the literature have two main limitations: first, the space and time dimensions have to be constrained by user-defined thresholds; second, elements (trajectory points) are clustered ignoring the trajectory sequence, assuming that the points are independent among them. To address the limitations above, we propose a new trajectory co-clustering method for mining semantic trajectory co-clusters. It simultaneously clusters the trajectories and their elements taking into account the order in which they appear. This new method uses the element frequency to identify candidate co-clusters. Besides, it uses an objective cost function that automatically drives the co-clustering process, avoiding the need for constraining dimensions. We evaluate the proposed approach using real-world a publicly available dataset. The experimental results show that our proposal finds frequent and meaningful contiguous sequences revealing mobility patterns, thereby the most relevant elements.
(Im)balance in the Representation of News? An Extensive Study on a Decade Long Dataset from India
Venue: arXiv cs.DL 2021
Year: 2021

(Im)balance in the representation of news has always been a topic of debate in political circles. The concept of balance has often been discussed and studied in the context of the social responsibility theory and the prestige press in the USA. While various qualitative, as well as quantitative measures of balance, have been suggested in the literature, a comprehensive analysis of all these measures across a large dataset of the post-truth era comprising different popular news media houses and over a sufficiently long temporal scale in a non-US democratic setting is lacking. We use this concept of balance to measure and understand the evolution of imbalance in Indian media on various journalistic metrics on a month-by-month basis. For this study, we amass a huge dataset of over four million political articles from India for 9+ years and analyze the extent and quality of coverage given to issues and political parties in the context of contemporary influential events for three leading newspapers. We use several state-of-the-art NLP tools to effectively understand political polarization (if any) manifesting in these articles over time. We find that two out of the three news outlets are more strongly clustered in their imbalance metrics. We also observe that only a few locations are extensively covered across all the news outlets and the situation is only slightly getting better for one of the three news outlets. Cloze tests show that the changing landscape of events get reflected in all the news outlets with border and terrorism issues dominating in around 2010 while economic aspects like unemployment, GST, demonetization, etc. became more dominant in the period 2014 -- 2018. Further, cloze tests clearly portray the changing popularity profile of the political parties over time.