Reviewerly/exHarmony huggingface dataset:
Time-Memory Trade-Offs for Saber+ on Memory-Constrained RISC-V Platform

Saber is a module-lattice-based key encapsulation scheme that has been selected as a finalist in the NIST Post-Quantum Cryptography standardization project. As Saber computes on considerably large matrices and vectors of polynomials, its efficient implementation on memory-constrained IoT devices is very challenging. In this paper, we present an implementation of Saber with a minor tweak to the original Saber protocol for achieving reduced memory consumption and better performance. We call this tweaked implementation ‘Saber+’, and the difference compared to Saber is that we use different generation methods of public matrix <inline-formula><tex-math notation="LaTeX">$\boldsymbol{A}$</tex-math></inline-formula> and secret vector <inline-formula><tex-math notation="LaTeX">$\boldsymbol{s}$</tex-math></inline-formula> for memory optimization. Our highly optimized software implementation of Saber+ on a memory-constrained RISC-V platform achieves 48% performance improvement compared with the best state-of-the-art memory-optimized implementation of original Saber. Specifically, we present various memory and performance optimizations for Saber+ on a memory-constrained RISC-V microcontroller, with merely 16KB of memory available. We utilize the Number Theoretic Transform (NTT) to speed up the polynomial multiplication in Saber+. For optimizing cycle counts and memory consumption during NTT, we carefully compare the efficiency of the complete and incomplete-NTTs, with platform-specific optimization. We implement 4-layers merging in the complete-NTT and 3-layers merging in the 6-layer incomplete-NTT. An improved on-the-fly generation strategy of the public matrix and secret vector in Saber+ results in low memory footprint. Furthermore, by combining different optimization strategies, various time-memory trade-offs are explored. Our software implementation for Saber+ on selected RISC-V core takes just 3,809K, 3,594K, and 3,193K clock cycles for key generation, encapsulation, and decapsulation, respectively, while consuming only 4.8KB of stack at most.
Who Speaks Like a Style of Vitamin: Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning

Abstractive dialogue summarization is a challenging task for several reasons. First, most of the important pieces of information in a conversation are scattered across utterances through multi-party interactions with different textual styles. Second, dialogues are often informal structures, wherein different individuals express personal perspectives, unlike text summarization, tasks that usually target formal documents such as news articles. To address these issues, we focused on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. To do this, we approach ad-hoc analysis to explore speakers’ text styles and constructed a syntax-aware model by leveraging linguistic information (i.e., POS tagging), which alleviates the above issues by inherently distinguishing sentences uttered from individual speakers. Our approach allows for both data and model-centric investigation. Also, we employed multi-task learning of both syntax-aware information and dialogue summarization. To the best of our knowledge, our approach is the first method to apply multi-task learning to the dialogue summarization task. Experiments on a SAMSum corpus (a large-scale dialogue summarization corpus) demonstrated that our method improved upon the vanilla model. Consequently, we found that our efforts of syntax-aware approach have been reflected by the model.
Robust phase unwrapping algorithm for interferometric applications based on Zernike polynomial fitting and Wrapped Kalman Filter

We propose a noise robust phase unwrapping algorithm based on the state-space model of Zernike polynomials fitting and the Wrapped Kalman Filter. In our method, the phase map is modeled as the linear combination of Zernike polynomials and the phase unwrapping problem reduces to the estimation of the fitting coefficients. To accurately calculate those coefficients under a noisy condition, the Wrapped Kalman Filter with a weighting strategy based on the second difference of the wrapped phase map is developed. As a result, our proposed method can successfully recover an absolute phase from a wrapped phase map corrupted by heavy noise. The results of synthetic data and experimental data show the outperformance of the proposed methods against other three representative algorithms.
Impacts of Blockchain on Digital Marketing

Blockchain adoption is covering the technological landscape at a rapid pace and its industrial uses are evolving across the entire industrial spectrum. Several technology giants and startups have initiated novel use cases of this technology amidst significant rise in investments in blockchain projects. Blockchain is perceived as an enabler to transform systems across multiple domains. Recent trends in digital marketing have witnessed extensive use of big data analytics and artificial intelligence in reshaping digital marketing. It is believed that the next big transformation in digital marketing shall be due to blockchain while proving to be a disruptive technology that will change the digital marketing process as we know it. Digital Marketing is all set to be significantly benefited with the use of blockchain in a similar manner like finance and banking industry did. This paper is an attempt to explore the evolution of blockchain and its potential impacts on digital marketing. Digital marketing appears to be evolving at a pace of information technology, but blockchain holds the potential to transform digital marketing in a riotous, perhaps even irrevocable manner.
An Elastic Collision Seeker Optimization Algorithm for Optimization Constrained Engineering Problems

To improve the seeker optimization algorithm (SOA), an elastic collision seeker optimization algorithm (ECSOA) was proposed. The ECSOA evolves some individuals in three situations: completely elastic collision, completely inelastic collision, and non-completely elastic collision. These strategies enhance the individuals’ diversity and avert falling into the local optimum. The ECSOA is compared with the particle swarm optimization (PSO), the simulated annealing and genetic algorithm (SA_GA), the gravitational search algorithm (GSA), the sine cosine algorithm (SCA), the multiverse optimizer (MVO), and the seeker optimization algorithm (SOA); then, fifteen benchmark functions, four PID control parameter models, and six constrained engineering optimization problems were selected for the experiment. According to the experimental results, the ECSOA can be used in the benchmark functions, the PID control parameter optimization, and the optimization constrained engineering problems. The optimization ability and robustness of ECSOA are better.
Evaluating and Explaining Generative Adversarial Networks for Continual Learning under Concept Drift

Generative Adversarial Networks (GANs) are among the most popular contemporary machine learning algorithms. Despite remarkable successes in their developments, existing GANs cannot offer the appropriate tools to monitor their performance in a continual learning scenario when data distribution changes. We propose a complete framework for monitoring and evaluating GANs during the continual learning, explaining their reaction to the data distribution shifts. The proposed approach is the first complete solution for evaluating GANs in drifting environments, additionally adding explainability to the adaptation process. We introduce a novel prequential metric for continual evaluation of GANs. We show how to use various information extracted from streaming GANs to understand the model&#x2019;s behavior under data changes and gain insight into the nature of concept drift. Our explainable components focus on learning curves under non-stationary data that highlight retaining relevant and forgetting outdated information, as well as of dynamic visualization of changes in relevant regions for drifting images. The proposed tool allows for detecting changes in data, as well as evaluating and explaining the reaction of GANs to the concept shift. Our framework can be downloaded from a public repository https://github.com/w4k2/gan-data-streams.
Configurable Agent With Reward As Input: A Play-Style Continuum Generation

Modern video games are becoming richer and more complex in terms of game mechanics. This complexity allows for the emergence of a wide variety of ways to play the game across the players. From the point of view of the game designer, this means that one needs to anticipate a lot of different ways the game could be played. Machine Learning (ML) could help address this issue. More precisely, Reinforcement Learning is a promising answer to the need of automating video game testing. In this paper we present a video game environment which lets us define multiple play-styles. We then introduce CARI: a Configurable Agent with Reward as Input. An agent able to simulate a wide continuum range of play-styles. It is not constrained to extreme archetypal behaviors like current methods using reward shaping. In addition it achieves this through a single training loop, instead of the usual one loop per play-style. We compare this novel training approach with the more classic reward shaping approach and conclude that CARI can also outperform the baseline on archetypes generation. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.
Explainable Reinforcement Learning for Human-Robot Collaboration

Reinforcement learning (RL) is getting popular in the robotics field due to its nature to learn from dynamic environments. However, it is unable to provide explanations of why an output was generated. Explainability becomes therefore important in situations where humans interact with robots, such as in human-robot collaboration (HRC) scenarios. Attempts to address explainability in robotics usually are restricted to explain a specific decision taken by the RL model, but not to understand the complete behavior of the robot. In addition, the explainability methods are restricted to be used by domain experts as queries and responses are not translated to natural language. This work overcomes these limitations by proposing an explainability solution for RL models applied to HRC. It is mainly formed by the adaptation of two methods: (i) Reward decomposition gives an insight into the factors that impacted the robot&#x0027;s choice by decomposing the reward function. It further provides sets of relevant reasons for each decision taken during the robot&#x0027;s operation; (ii) Autonomous policy explanation provides a global explanation of the robot&#x0027;s behavior by answering queries in the form of natural language, thus making understandable to any human user. Experiments in simulated HRC scenarios revealed an increased understanding of the optimal choices made by the robots. Additionally, our solution demonstrated as a powerful debugging tool to find weaknesses in the robot&#x0027;s policy and assist in its improvement.
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees

After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees.We introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR). We perform empirical evaluation via simulations and a real-world study predicting Chronic Obstructive Pulmonary Disease (COPD) risk. We derive "Type I and II" regret bounds, which guarantee the procedures are noninferior to a static model and competitive with an oracle logistic reviser in terms of the average loss.Both procedures consistently outperformed the static model and other online logistic revision methods. In simulations, the average estimated calibration index (aECI) of the original model was 0.828 (95%CI, 0.818-0.938). Online recalibration using BLR and MarBLR improved the aECI towards the ideal value of zero, attaining 0.265 (95%CI, 0.230-0.300) and 0.241 (95%CI, 0.216-0.266), respectively. When performing more extensive logistic model revisions, BLR and MarBLR increased the average area under the receiver-operating characteristic curve (aAUC) from 0.767 (95%CI, 0.765-0.769) to 0.800 (95%CI, 0.798-0.802) and 0.799 (95%CI, 0.797-0.801), respectively, in stationary settings and protected against substantial model decay. In the COPD study, BLR and MarBLR dynamically combined the original model with a continually refitted gradient boosted tree to achieve aAUCs of 0.924 (95%CI, 0.913-0.935) and 0.925 (95%CI, 0.914-0.935), compared to the static model's aAUC of 0.904 (95%CI, 0.892-0.916).Despite its simplicity, BLR is highly competitive with MarBLR. MarBLR outperforms BLR when its prior better reflects the data.BLR and MarBLR can improve the transportability of clinical prediction models and maintain their performance over time.
Development of Real-Time Eye Tracking Algorithm

This project develops a real-time eye tracking algorithm to improve the accuracy of iris detection and gaze classification. Eye tracking has been utilized in many applications to detect driver awareness, predict human behaviour, and assist paralyzed individuals. Detecting the centre of iris is a crucial step in eye tracking development and Conventional Circular Hough Transform (CCHT) is widely used for the iris detection. However, the accuracy of the CCHT method decreases when the head of an object is not orthogonally positioned to the camera under ambient light changes. To overcome this problem, the facial landmark detector is implemented to detect the eyes as the Region of Interest (ROI), track the eyes and identify the gaze by classifying the eye positions to left, right and middle. The eye tracking algorithm has an added feature to detect eye blinking for drowsiness detection. To gain better accuracy, the classifier uses a scan method to classify the eye position based on the levels of pixel intensity. The eye tracking algorithm is implemented in OpenCV using Python Software for ease of portability. The results show that the average accuracy of 100% and 90% are achieved in the iris detection and gaze position classification, respectively.
Do You Ever Get Off Track in a Conversation? The Conversational System’s Anatomy and Evaluation Metrics

Conversational systems are now applicable to almost every business domain. Evaluation is an important step in the creation of dialog systems so that they may be readily tested and prototyped. There is no universally agreed upon metric for evaluating all dialog systems. Human evaluation, which is not computerized, is now the most effective and complete evaluation approach. Data gathering and analysis are evaluation activities that need human intervention. In this work, we address the many types of dialog systems and the assessment methods that may be used with them. The benefits and drawbacks of each sort of evaluation approach are also explored, which could better help us understand the expectations associated with developing an automated evaluation system. The objective of this study is to investigate conversational agents, their design approaches and evaluation metrics. This approach can help us to better understand the overall process of dialog system development, and future possibilities to enhance user experience. Because human assessment is costly and time consuming, we emphasize the need of having a generally recognized and automated evaluation model for conversational systems, which may significantly minimize the amount of time required for analysis.
Staging with class: a specification for typed template Haskell

Multi-stage programming using typed code quotation is an established technique for writing optimizing code generators with strong type-safety guarantees. Unfortunately, quotation in Haskell interacts poorly with type classes, making it difficult to write robust multi-stage programs. We study this unsound interaction and propose a resolution, staged type class constraints, which we formalize in a source calculus λ ⇒ that elaborates into an explicit core calculus F . We show type soundness of both calculi, establishing that well-typed, well-staged source programs always elaborate to well-typed, well-staged core programs, and prove beta and eta rules for code quotations. Our design allows programmers to incorporate type classes into multi-stage programs with confidence. Although motivated by Haskell, it is also suitable as a foundation for other languages that support both overloading and quotation.
Interactive Gated Decoder for Machine Reading Comprehension

Owing to the availability of various large-scale Machine Reading Comprehension ( MRC ) datasets, building an effective model to extract passage spans for question answering has been well studied in previous works. However, in reality, there are some questions that cannot be answered through the passage information, which brings more challenges to this task. In this article, we propose an Interactive Gated Decoder ( IG Decoder ), which focuses on modeling the interactions between the answer span prediction and no-answer prediction with a gating mechanism. We also propose a simple but effective approach for automatically generating pseudo training data, which aims to enrich the training data of the unanswerable questions. Experimental results on popular benchmark SQuAD 2.0 and NewsQA show that the proposed approaches yield consistent improvements over traditional BERT-large and strong ALBERT-xxlarge baseline systems. We also provide detailed ablations of the proposed method and error analysis on hard samples, which could be helpful in future research.
Unconstrained Power Management Algorithm for Green Cloud Computing

Green cloud computing (GCC) is an emerging trend to reduce the environmental impact of information and communications technology (ICT) devices. Here, power management plays a vital role in providing potential solutions. It helps to ensure efficient, safe and reliable electric distribution to the devices connected to it. In general, the power supply of datacenters is connected to the grid, which is operated using non-renewable or brown energy (NRE) sources (like fossil fuels). These sources will not be replenished and run out over time. Moreover, they are harmful to the environment by releasing carbon dioxide, heat and other gasses. Subsequently, the power supply of datacenters is connected to renewable or green energy (RE) sources (like solar) in order to reduce the environmental impact. However, these sources are less reliable and seasonal. Therefore, nowadays, datacenters use both NRE and RE sources to make a trade-off. In this paper, we propose unconstrained power management (UPM) algorithm for GCC. We consider three power supplies, namely grid, PV and battery, to handle the load power demand of the user requests (URs) submitted to the datacenters. The proposed algorithm tries to fulfill such demand using RE sources. In case of scarcity in RE sources, it manages the load power using NRE sources. We simulate the proposed algorithm by considering three scenarios, namely NRE sources, RE sources and both, using five different instances. The simulation results are compared and analyzed in terms of three performance metrics, namely overall cost (OC), UR assigned to NRE (UNRE) and UR assigned to RE (URE), to show the applicability of the proposed algorithm under three different scenarios.
A Conceptual Model to Identify Illegal Activities on the Bitcoin System

Soon after its inception in 2009, Bitcoin was used as a tool by malicious attackers who exploit its pseudo-anonymity to establish untraceable frauds. Recently, several Bitcoin users and institutions have confirmed that thousands of Bitcoins were lost due to the failure to implement a fraud detection system, causing significant damage to individuals or institutions and resulting in bankruptcy. The anonymous nature of the Bitcoin system makes it a desirable option for malicious people to carry out illegal activities, making it difficult for law enforcement to detect suspicious behavior and making the current fraud detection techniques impractical. Thus, identifying illegal activities becomes an important factor to protect the reputation of the Bitcoin system. In this paper, we propose a model to identify illegal transactions in the Bitcoin system. Firstly, we collect illegal addresses for data labeling purposes from different sources such as online public bitcoin forums and related datasets from previous papers and then verify them with a raw Bitcoin dataset. Secondly, we introduce new types of features by using a time-based approach to segment transactions into time slices over a period in addition to the most meaningful features of the prior studies. Thirdly, we evaluate the proposed model on five popular supervised classifiers (KNN, SVM, RF, XGB, and KNN). Finally, this paper considers the problem of class imbalance and attained better optimization when using an adaptive oversampling technique (ADASYN). Results obtained from this study demonstrate that RF and XGB outperform KNN, SVM, and NN in terms of detection rate.
xDNN: Inference for Deep Convolutional Neural Networks

We present xDNN, an end-to-end system for deep-learning inference based on a family of specialized hardware processors synthesized on Field-Programmable Gate Array (FPGAs) and Convolution Neural Networks (CNN). We present a design optimized for low latency, high throughput, and high compute efficiency with no batching. The design is scalable and a parametric function of the number of multiply-accumulate units, on-chip memory hierarchy, and numerical precision. The design can produce a scale-down processor for embedded devices, replicated to produce more cores for larger devices, or resized to optimize efficiency. On Xilinx Virtex Ultrascale+ VU13P FPGA, we achieve 800 MHz that is close to the Digital Signal Processing maximum frequency and above 80% efficiency of on-chip compute resources. On top of our processor family, we present a runtime system enabling the execution of different networks for different input sizes (i.e., from 224× 224 to 2048× 1024). We present a compiler that reads CNNs from native frameworks (i.e., MXNet, Caffe, Keras, and Tensorflow), optimizes them, generates codes, and provides performance estimates. The compiler combines quantization information from the native environment and optimizations to feed the runtime with code as efficient as any hardware expert could write. We present tools partitioning a CNN into subgraphs for the division of work to CPU cores and FPGAs. Notice that the software will not change when or if the FPGA design becomes an ASIC, making our work vertical and not just a proof-of-concept FPGA project. We show experimental results for accuracy, latency, and power for several networks: In summary, we can achieve up to 4 times higher throughput, 3 times better power efficiency than the GPUs, and up to 20 times higher throughput than the latest CPUs. To our knowledge, we provide solutions faster than any previous FPGA-based solutions and comparable to any other top-of-the-shelves solutions.