Reviewerly/exHarmony huggingface dataset:
Analysis of Graph Processing in Reconfigurable Devices for Edge Computing Applications

Graph processing is an area that has received significant attention in recent years due to the substantial expansion in industries relying on data analytics. Alongside the vital role of finding relations in social networks, graph processing is also widely used in transportation to find optimal routes and biological networks to analyse sequences. The main bottleneck in graph processing is irregular memory accesses rather than computation intensity. Since computational intensity is not a driving factor, we propose a method to perform graph processing at the edge more efficiently. We believe current cloud computing solutions are still very costly and have latency issues. The results demonstrate the benefits of a dedicated sparse graph processing algorithm compared with dense graph processing when analysing data with low density. As graph datasets grow exponentially, traversal algorithms such as breadth-first search (BFS), fundamental to many graph processing applications and metrics, become more costly to compute. Our work focuses on reviewing other implementations of breadth-first search algorithms designed for low power systems and proposing our solution that utilises advanced enhancements to achieve a significant performance boost up to 9.2x better performance in terms of MTEPS compared to other state-of-the-art solutions with a power usage of 2.32W.
An Approach for Improved Data matrix distortion correction using Median filtered Hough Transform

Data matrix is a two-dimensional barcode, widely used to store large amounts of information in a limited space. These data matrices are printed on labels or etched on surfaces where they are often subjected to various distortions and harsh conditions. This makes the data matrix unreliable and undetectable which calls for a need for effective and accurate detection. In this paper, an efficient image processing algorithm is presented to correct the distortion that utilizes a combination of median filter and Hough Line Transform to improve the accuracy of detection. Primarily, the raw image is preprocessed with a median filter to improve the quality of the hough line transform. The peak detected by the hough line transform corresponds to the angle of tilt necessary to be applied to the data matrix to correct the geometric distortion. The data matrix is then cropped and aligned according to the orientation of the finder patterns for further reconstruction. This implementation has been tested on a dataset containing 80 data matrices which have been curated by subjecting to various noises and distortion. Results indicate that the algorithm performs efficiently on the distorted images and produces accurate results with an error rate of 2.7% in the reconstructed modules of data matrices. Thus, the proposed framework can be implemented to recognize real-time distorted data matrix codes.
Is the Whole lesser than its Parts? Breaking an Aggregation based Privacy aware Metering Algorithm

Smart metering is a mechanism through which fine-grained electricity usage data of consumers is collected periodically in a smart grid. However, a growing concern in this regard is that the leakage of consumers' consumption data may reveal their daily life patterns as the state-of-the-art metering strategies lack adequate security and privacy measures. Many proposed solutions have demonstrated how the aggregated metering information can be transformed to obscure individual consumption patterns without affecting the intended semantics of smart grid operations. In this paper, we expose a complete break of such an existing privacy preserving metering scheme [10] by determining individual consumption patterns efficiently, thus compromising its privacy guarantees. The underlying methodol-ogy of this scheme allows us to - i) retrieve the lower bounds of the privacy parameters and ii) establish a relationship between the privacy preserved output readings and the initial input readings. Subsequently, we present a rigorous experimental validation of our proposed attacking methodology using real-life dataset to highlight its efficacy. In summary, the present paper queries: Is the Whole lesser than its Parts? for such privacy aware metering algorithms which attempt to reduce the information leakage of aggregated consumption patterns of the individuals.
Multilingual Text-Based Image Search Using Multimodal Embeddings

The explosion of data and information on the Internet calls for efficient and reliable information retrieval methods. While textual information retrieval systems have significantly improved, content-based image retrieval using text inputs requires further study and optimization. This research paper proposes a system that uses CLIP (Contrastive Language-Image Pre-Training) model, which projects images and text into a multimodal embedding space to provide representations to compare the semantic meaning of words to embeddings of the image in a dataset. The output is a set of images closely resembling the text input, which is achieved through cosine similarity based on matrix operations. The models have also been optimized for use in production with the use of ONNX runtime is done to speed up inference timing. The application is full-stack and easily accessible, with a ReactJS frontend hosted on Netlify and Flask based Python backend hosted on AWS.
Application of Reinforcement Learning to The Orientation and Position Control of A 6 Degrees of Freedom Robotic Manipulator

Applications with autonomous robots play an important role in the industry and in everyday life. Among them, the activities of manipulating and moving objects are highlighted by the wide variety of possible applications. These activities in static and known environments can be implemented through logic planned by the developer, but this is not feasible in dynamic environments. Machine Learning (ML) techniques such as Reinforcement Learning (RL) algorithms have sought to replace the pre-defined programming by teaching the robot how to act. This paper presents the implementation of two RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), for orientation and position control of a 6-degree-of-freedom (6-DoF) robotic manipulator. The results demonstrated that the DDPG have a faster learning convergence in simpler activities, but if the complexity of the problem increases, it might not obtain a satisfactory behavior. On the other hand, PPO can solve more complex problems but it limits the convergence rate to the best result in order to avoid learning instability.
Towards Fine-grained Side-Channel Instruction Disassembly on a System-on-Chip

Side-channel based instruction disassembly (SCBD) is a family of side-channel attacks that aims at recovering the code executed by a device from physical measurements. Over past decades researches have proved that instruction-level disassembly is feasible on simple controllers. Simultaneously, the computing power and architectural complexity of processors are increasing, even in constrained devices. Performing side-channel attacks on mid or high-end devices is inherently harder because of complex concurrent activities and an important amount of noise. While broad pattern identification, such as cryptographic primitives, has been proved possible, the feasibility of precise SCBD remains an open question on a complex System-on-Chip (SoC). In this work, we address some of the technical challenges involved in performing SCBD on SoCs. We propose an experimental setup and measurement methodology that enables reliable characterization of instruction-level electromagnetic (EM) leakages. We study the feasibility of three code reconstruction granularities: functional unit recognition, opcode recognition and full instruction recovery. Under a controlled experimental environment, our results show that functional unit recognition is achievable (100% classification accuracy) as well as opcode recognition (with evidence of leakage). In our setup, full instruction recovery (i.e., bit-level encoding) turned out to be more challenging. We show that the classification accuracy on instruction bits is better than random guesses and can be improved by combining multiple EM probe positions, but it is not high enough to foresee an attack in a real environment.
Omnidirectional Autonomous Aggressive Perching of Unmanned Aerial Vehicle using Reinforcement Learning Trajectory Generation and Control

Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. We demonstrate the performance of the trained reinforcement learning controller generated trajectory information and manipulated quadrotor toward the perching point (manually throwing it up in the air with an initial velocity of 1 m/s). We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with relatively accurate. Computation time of evaluating the policy is only 0.03 sec per trajectory, which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.
Differential Evolution Using Surrogate Model Based on Pairwise Ranking Estimation for Constrained Optimization Problems

This study proposes a new surrogate-assisted evolutionary algorithm, CHDE+ELDR, that combines CHDE, a constraint-handling evolutionary algorithm, and ELDR, a pairwise ranking surrogate model. The experiment compares CHDE+ELDR with CHDE without a surrogate model on 13 constrained optimization benchmark problems. The experimental results show that CHDE+ELDR significantly outperforms CHDE without a surrogate model on all benchmark problems.
An FPGA based Tiled Systolic Array Generator to Accelerate CNNs

The main computation in any CNN is convolution operation. This computation shows significant potential for massively parallel implementations on an FPGA. Systolic arrays with their intrinsic pipelining have been explored for CNN inference. In this paper, we present a systolic array architecture suitably designed for a novel method of convolution operation. We implement an image-kernel convolution and test it with representative image inputs to several models like LeNet-5, AlexNet, VGG-16, and Resnet-34. We compare the proposed design with conventional convolution and HLS based designs. We limit our implementation to resource constrained FPGA: AMD-Xilinx Zynq 7020 platform. We observe that the proposed architecture outperforms the direct convolution method and HLS pipelined designs by 2× and 2.1×, respectively, on average. Since DSP blocks are scarce resources, we constrain our implementation to avoid DSP blocks and use the LUTs instead. Thus, our implementation uses nearly 9× more LUTs than baseline convolution but 8× fewer LUTs than the HLS pipelined implementation. We further accelerate the convolution throughput by 11×. We achieve this by implementing a tiled systolic architecture that completely utilises the parallel computing resources of the FPGA.
Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots

Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide variety of robots. Previous works showed that these Deep-RL algorithms can be applied to perform mapless navigation of mobile robots in general. However, they tend to use simple sensing strategies since it has been shown that they perform poorly with a high dimensional state spaces, such as the ones yielded from image-based sensing. This paper presents a comparative analysis of two Deep-RL techniques - Deep Deterministic Policy Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing tasks of mapless navigation for mobile robots. We aim to contribute by showing how the neural network architecture influences the learning itself, presenting quantitative results based on the time and distance of navigation of aerial mobile robots for each approach. Overall, our analysis of six distinct architectures highlights that the stochastic approach (SAC) better suits with deeper architectures, while the opposite happens with the deterministic approach (DDPG).
Partial Evaluation in Junction Trees

One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.
OMR Card Scoring Method Using Template Matching

In this paper, we propose an algorithm that recognizes and scores the answer by photographing the marked OMR card with a mobile phone for individual use. First, the area of the answer is extracted from the OMR card. After presenting the OMR scoring method of the two methods, the two methods are compared and analyzed. The first algorithm is a method of finding marked and unmarked questions using the marked area as a template, and sorting them to extract the correct answer. The second algorithm is a method of extracting the correct answer by comparing the coordinate values of the marked answer and the answer number for each question using all item numbers and the marked area as a template.
Quantization: how far should we go?

Machine learning, and specifically Deep Neural Networks (DNNs) impact all parts of daily life. Although DNNs can be large and compute intensive, requiring processing on big servers (like in the cloud), we see a move of DNNs into loT-edge based systems, adding intelligence to these systems. These systems are often energy constrained and too small for satisfying the huge DNN computation and memory demands. DNN model quantization may come to the rescue. Instead of using 32-bit floating point numbers, much smaller formats can be used, down to 1-bit binary numbers. Although this largely may solve the compute and memory problems, it comes with a huge price, model accuracy reduction. This problem spawned a lot of research into model repair methods, especially for binary neural networks. Heavy quantization triggers a lot of debate; we even see some movements of going back to higher precision using brainfloats. This paper therefore evaluates the trade-off between energy reduction through extreme quantization versus accuracy loss. This evaluation is based on ResNet-I8 with the ImageNet dataset, mapped to a fully programmable architecture with special support for 8-bit and 1-bit deep learning, the BrainTTA. We show that, after applying repair methods, the use of extremely quantized DNNs makes sense. They have superior energy efficiency compared to DNNs based on 8-bit precision of weights and data, while only having a slightly lower accuracy. There is still an accuracy gap, requiring further research, but results are promising. A side effect of the much lower energy requirements of BNNs is that external DRAM becomes more dominant. This certainly requires further attention.
Phatic communication and its implications for online learning motivation

The phatic expression or phatic category is an aspect of speech that is functionally contained in oral speech which is used to initiate, maintain, and strengthen the conversation. In learning, phatic speech can be observed in the speech of teachers and students. This is very closely related to improving the psychological atmosphere in the classroom. The purpose of this study is to describe, test, and analyze the effect of the phatic category on learning motivation based on competence motivation, namely the willingness to work hard, enthusiasm for involving students in communication, and the spirit of independent learning. This research was conducted by testing the hypothesis that there was a significant effect of the phatic category in the lecturer's speech on student learning motivation. The data were analyzed by t-test technique in the class with high phatic expression and low phatic expression, witthe number of each class 35 students. Data collection was applied with a questionnaire to determine the implications of lecturer phatic expressions on student motivation. Each function in the phatic expression consists of 2 questions with a scale of 1: less, 2: enough, and 3: good. In addition, there is a search for phatic expressions functions by measuring the percentage of each function. The results of the normality test of learning motivation data in the high and low phatic expressions groups showed that the results were not normally distributed, with a significance of 0.000 and 0.010. Thus, the statistical test of the t-test hypothesis was carried out using the Mann-Whitney test technique. From the analysis of the Mann Whitney test, it was found that the difference in the level of learning motivation between the high class and low class phatic was significant (0.037< 0.05). Thus, it can be concluded that there is a significant influence of lecturer phatic expressions on student learning motivation. Meanwhile, from the percentage test of the phatic function, the data obtained that the desired type of phatic speech is the phatic utterance of sustaining.
Self-supervised learning exposure correction via histogram equalization prior

Poor lighting conditions in the real world may lead to ill-exposure in captured images which suffer from compromised aesthetic quality and information loss for post-processing. Recent exposure correction works address this problem by learning the mapping from images of multiple exposure intensities to well-exposed images. However, it requires a large number of paired training data, which is hard to implement for certain data-inaccessible scenarios. This paper presents a highly robust exposure correction method based on self-supervised learning. Specifically, two sub-networks are designed to deal with under- and over-exposed regions in ill-exposed images respectively. This hybrid architecture enables adaptive ill-exposure correction. Then, a fusion module is employed to fuse the under-exposure corrected image and the over-exposure corrected image to obtain a well-exposed image with vivid color and clear textures. Notably, the training process is guided by histogram-equalized images with the application of histogram equalization prior (HEP), which means that the presented method only requires ill-exposed images as training data. Extensive experiments on real-world image datasets validate the robustness and superiority of this technique.
Aspect-Sentiment-based Opinion Summarization using Multiple Information Sources

Opinion summarization is the task of summarizing opinions expressed for an entity or a product. Traditionally, all approaches use customer reviews or ratings to extract opinions and generate summaries. In this work, we propose using four sources of information: product description, product specification, customer reviews, and question-answers for aspect-sentiment-based opinion summarization. We use a transfer-learning approach to fine-tune a pre-trained model to generate summaries. Our experiments demonstrate the importance of all the four information sources resulting in improved quality of summaries. More specifically, product description and customer reviews contribute to detecting aspects and generating aspect-level and general summaries. Product specifications help in enriching the aspects whereas question-answers provide additional information. We make our annotated dataset containing the four information sources publicly available.
BRR-QA: Boosting Ranking and Reading in Open-Domain Question Answering

Open-domain question qnswering (OpenQA) involves a retriever for selecting relevant passages from large text corpora (e.g. Wikipedia) and a reading comprehension (RC) model for extracting answers from these retrieved passages. The retrieved passages are often noisy. Since OpenQA relies heavily on efficient passages for better answer prediction, many passage ranker models have been proposed to filter out noisy passages. However, their performance is limited because their ranker model scores each passage separately by modelling only the relationship between query and passage. Thus, they could not capture local context information. Their ranker model also ignored the rich initial rank of passages ranked by a search engine. This paper presents a Passage Ranker model that captures local-context information through cross-passage interaction. Our ranker model integrates initial ranking and uses modified attention in the cross-passage interaction to compute a better confidence score for each passage. Moreover, we integrate SRL into our passage reader and train it on proposed sampled data. Our semantic reader can absorb contextual semantics. Experimental results on four public OpenQA datasets show that our model significantly outperforms recent OpenQA baselines.
Missing Data Resilient Ensemble Subspace Decision Tree Classifier

The construction of a decision tree on a given data set involves certain features. In case these features have NULL values during the production environment, the decision tree performance suffers. In this work, we develop a methodology to generate subspace decision tree classifiers, where each decision tree uses non-overlapping subsets of feature sets. We build an ensemble classifier over these subspace classifiers to get better performance. We consider different levels of missing data in our ensemble classifier, to show that it is resilient to missing data and can also give better performance even in cases of incomplete data (that is, data with missing values). We show the viability of our methodology using real-life and synthetic data sets. Our approach delivers a simple yet effective missing data resilient decision tree classifier.
Customer Informatics by Embedding SMS Headers

Short Message Service (SMS) headers are alpha-numeric codes which identify the message sender without divulging the contents of the message. The code by itself may be uninformative of the intent of the message or the interest of the recipient. We show the application of embedding techniques in learning representations of the SMS headers for commercial communication. We use these embeddings to 1) discover insightful header cohorts and, 2) create customer embeddings which are then applied as features in supervised modeling tasks such as lookalike modelling and gender prediction. The experimental results show the customer embeddings help in improving performance of these models and also emerge as top features. This derived intelligence improves customer experience, product offerings and advertisement yield. To the best of our knowledge, this is the first application of representation learning for SMS headers.