Reviewerly/exHarmony huggingface dataset:
An Insightful Overview of the Wiener Filter for System Identification

Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice. Moreover, it also represents a benchmark for other related optimization problems. In this paper, new insights into the regularization of the Wiener filter are provided, which is a must in real-world scenarios. A proper regularization technique is of great importance, especially in challenging conditions, e.g., when operating in noisy environments and/or when only a low quantity of data is available for the estimation of the statistics. Different regularization methods are investigated in this paper, including several new solutions that fit very well for the identification of sparse and low-rank systems. Experimental results support the theoretical developments and indicate the efficiency of the proposed techniques.
Complementing Privacy and Utility Trade-Off with Self-Organising Maps

In recent years, data-enabled technologies have intensified the rate and scale at which organisations collect and analyse data. Data mining techniques are applied to realise the full potential of large-scale data analysis. These techniques are highly efficient in sifting through big data to extract hidden knowledge and assist evidence-based decisions, offering significant benefits to their adopters. However, this capability is constrained by important legal, ethical and reputational concerns. These concerns arise because they can be exploited to allow inferences to be made on sensitive data, thus posing severe threats to individuals’ privacy. Studies have shown Privacy-Preserving Data Mining (PPDM) can adequately address this privacy risk and permit knowledge extraction in mining processes. Several published works in this area have utilised clustering techniques to enforce anonymisation models on private data, which work by grouping the data into clusters using a quality measure and generalising the data in each group separately to achieve an anonymisation threshold. However, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to complement this balancing act by optimising utility in PPDM processes. To illustrate this, we propose a hybrid approach, that combines self-organising maps with conventional privacy-based clustering algorithms. We demonstrate through experimental evaluation, that results from our approach produce more utility for data mining tasks and outperforms conventional privacy-based clustering algorithms. This approach can significantly enable large-scale analysis of data in a privacy-preserving and trustworthy manner.
Image Denoising Based on Fractional Gradient Vector Flow and Overlapping Group Sparsity as Priors

In this paper, a new regularization term in the form of L1-norm based fractional gradient vector flow (LF-GGVF) is presented for the task of image denoising. A fractional order variational method is formulated, which is then utilized for estimating the proposed LF-GGVF. Overlapping group sparsity along with LF-GGVF is used as priors in image denoising optimization framework. The Riemann-Liouville derivative is used for approximating the fractional order derivatives present in the optimization framework. Its role in the framework helps in boosting the denoising performance. The numerical optimization is performed in an alternating manner using the well-known alternating direction method of multipliers (ADMM) and split Bregman techniques. The resulting system of linear equations is then solved using an efficient numerical scheme. A variety of simulated data that includes test images contaminated by additive white Gaussian noise are used for experimental validation. The results of numerical solutions obtained from experimental work demonstrate that the performance of the proposed approach in terms of noise suppression and edge preservation is better when compared with that of several other methods.
Handling new target classes in semantic segmentation with domain adaptation

In this work, we define and address a novel domain adaptation (DA) problem in semantic scene segmentation, where the target domain not only exhibits a data distribution shift w.r.t. the source domain, but also includes novel classes that do not exist in the latter. Different to “open-set” (Panareda Busto and Gall, 2017) and “universal domain adaptation” (You et al. 2019), which both regard all objects from new classes as “unknown”, we aim at explicit test-time prediction for these new classes. To reach this goal, we propose a framework that leverages domain adaptation and zero-shot learning techniques to enable “boundless” adaptation in the target domain. It relies on a novel architecture, along with a dedicated learning scheme, to bridge the source–target domain gap while learning how to map new classes’ labels to relevant visual representations. The performance is further improved using self-training on target-domain pseudo-labels. For validation, we consider different domain adaptation set-ups, namely synthetic-2-real, country-2-country and dataset-2-dataset. Our framework outperforms the baselines by significant margins, setting competitive standards on all benchmarks for the new task. Code and models are available at: https://github.com/valeoai/buda.
Class Incremental Learning for Video Action Classification

Class Incremental Learning (CIL) is a hot topic in machine learning for CNN models to learn new classes incrementally. However, most of the CIL studies are for image classification and object recognition tasks and few CIL studies are available for video action classification. To mitigate this problem, in this paper, we present a new Grow When Required network (GWR) based video CIL framework for action classification. GWR learns knowledge incrementally by modeling the manifold of video frames for each encountered action class in feature space. We also introduce a Knowledge Consolidation (KC) method to separate the feature manifolds of old class and new class and introduce an associative matrix for label prediction. Experimental results on KTH and Weizmann demonstrate the effectiveness of the framework.
O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed

The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain specific DNN accelerators, among which optical DNN accelerators are particularly promising thanks to their unprecedented potential of achieving superior performance-per-watt. However, the development of optical DNN accelerators is much slower than that of electrical DNN accelerators. One key challenge is that while many techniques have been developed to facilitate the development of electrical DNN accelerators, techniques that support or expedite optical DNN accelerator design remain much less explored, limiting both the achievable performance and the innovation development of optical DNN accelerators. To this end, we develop the first-of-its-kind framework dubbed O-HAS, which for the first time demonstrates automated Optical Hardware Accelerator Search for boosting both the acceleration efficiency and development speed of optical DNN accelerators. Specifically, our O-HAS consists of two integrated enablers: (1) an O-Cost Predictor, which can accurately yet efficiently predict an optical accelerator's energy and latency based on the DNN model parameters and the optical accelerator design; and (2) an O-Search Engine, which can automatically explore the large design space of optical DNN accelerators and identify the optimal accelerators (i.e., the micro-architectures and algorithm-to-accelerator mapping methods) in order to maximize the target acceleration efficiency. Extensive experiments and ablation studies consistently validate the effectiveness of both our O-Cost Predictor and O-Search Engine as well as the excellent efficiency of O-HAS generated optical accelerators.
Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review

Surgery is an effective but underused treatment for drug-resistant epilepsy in children. Algorithms to identify surgical candidates and estimate the likelihood of postoperative clinical improvement may be valuable to improve access to epilepsy surgery. We provide a focused review of these approaches. For adults with epilepsy, tools to identify surgical candidates and predict seizure and cognitive outcomes (Ie, Cases for Epilepsy (toolsforepilepsy.com) and Epilepsy Surgery Grading Scale) have been validated and are in use. Analogous tools for children need development. A promising approach is to apply statistical learning tools to clinical datasets, such as electroencephalogram tracings, imaging studies, and the text of clinician notes. Demonstration projects suggest these techniques have the potential to be highly accurate, and await further validation and clinical application.
A Discrete Scheme for Computing Image’s Weighted Gaussian Curvature

Weighted Gaussian curvature is an important smoothness measurement for images. However, its conventional computation scheme has low performance, low accuracy and requires that the input image must be second order differentiable. To tackle these three issues, we propose a novel discrete computation scheme for the weighted Gaussian curvature. Our scheme does not require the second order differentiability. Moreover, our scheme is more accurate, has smaller support region and computationally more efficient than the conventional schemes. Therefore, our scheme holds promise for a large range of applications where the weighted Gaussian curvature is needed, for example, image smoothing, cartoon texture decomposition, optical flow estimation, etc.
Secure Open Fronthaul Interface for 5G Networks

The open fronthaul interface is a standard protocol for a link between the radio units and the distributed unit in RAN, enabling different vendors interoperable. We study the security requirements of the open fronthaul interface for 5G networks. The O-RAN management plane (M-plane) mandates an end-to-end security using SSHv2, whereas the O-RAN control and user plane (CU-plane) do not support any security measure yet. We investigate MACsec for the CU-plane security, which is recommended as one of security options in the eCPRI specification. Furthermore, we implemented quantum-safe crypto solutions using a hybrid mode key exchange and signature schemes, which can be applied for the post-quantum SSH and MACsec protocols.
Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

Humans use spatial language to naturally describe object locations and their relations. Interpreting spatial language not only adds a perceptual modality for robots, but also reduces the barrier of interfacing with humans. Previous work primarily considers spatial language as goal specification for instruction following tasks in fully observable domains, often paired with reference paths for reward-based learning. However, spatial language is inherently subjective and potentially ambiguous or misleading. Hence, in this paper, we consider spatial language as a form of stochastic observation. We propose SLOOP (Spatial Language Object-Oriented POMDP), a new framework for partially observable decision making with a probabilistic observation model for spatial language. We apply SLOOP to object search in city-scale environments. To interpret ambiguous, context-dependent prepositions (e.g. front), we design a simple convolutional neural network that predicts the language provider’s latent frame of reference (FoR) given the environment context. Search strategies are computed via an online POMDP planner based on Monte Carlo Tree Search. Evaluation based on crowdsourced language data, collected over areas of five cities in OpenStreetMap, shows that our approach achieves faster search and higher success rate compared to baselines, with a wider margin as the spatial language becomes more complex. Finally, we demonstrate the proposed method in AirSim, a realistic simulator where a drone is tasked to find cars in a neighborhood environment.
Breaking the Walls of Silence: Analyzing Criminal Investigations to Improve Our Understanding of Cybercrime

A major problem in cybercrime research is that it is simply difficult to study the criminals committing cybercrimes: Criminals try to hide their illegal activities and are usually not disposed to talking openly to researchers, which limits the usefulness of the more traditional social sciences’ research methods. Furthermore, the innovative methods used in cybercrime research come with their own limitations: Although they usually provide big datasets with sometimes hundreds of thousands of data points, they only provide superficial data or, in the case of scraped data, only contain information about one very small step in the crime scrips of cybercriminals. Police investigations, therefore, provide unique knowledge about criminal networks and their members due to the wide use of intrusive investigative methods such as wiretaps and IP taps, observations, undercover policing, and house searches. This chapter describes the pros and cons of using police investigations to shed light on cybercrimes.
Research on a Costmap that can Change the Turning Path of Mobile Robot

Abstract In the field of mobile robots based on ROS (Robot Operating System) platforms, A*, Dijkstra, and BFS (Breadth First Search) are widely used to obtain the shortest path. This approach, however, often results in paths that are close to the obstacles located in corners. In practical applications, cumulative positioning errors in the navigation process are produced by aging components, skidding wheels and so on. In addition, the local path planning of the robot does not completely follow the global path planning, so the robot may easily cross the inflation of the costmap and collide with obstacles when turning. This paper proposes a method to change the turning path of the robot by adding virtual obstacles to the obstacles in map corners. A global path is generated by the path planning algorithm along the virtual obstacle so that the robot can travel along a smooth track that is far from the obstacle when turning. The proposed algorithm ensures that the robot remains far from physical obstacles and avoids collisions.
Bm3d Vs 2-Layer Onn

Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient methods such as BM3D remain a popular choice, especially in resource-constrained scenarios. In this study, we aim to find out whether compact neural networks can learn to produce competitive results as compared to BM3D for AWGN image denoising. To this end, we conFigure networks with only two hidden layers and employ different neuron models and layer widths for comparing the performance with BM3D across different AWGN noise levels. Our results conclusively show that the recently proposed self-organized variant of operational neural networks based on a generative neuron model (Self-ONNs) is not only a better choice as compared to CNNs, but also provide competitive results as compared to BM3D and even significantly surpass it for high noise levels.
Implementation Chatbot Whatsapp using Python Programming for Broadcast and Reply Message Automatically

The use of VPS (Virtual Private Server) in Indonesia is still very expensive. The Chatbot application system is very important in the marketing field, especially for disseminating information directly and acceptable to many users at a time. This paper focused on using the WhatsApp application for the Chatbot system. This Chatbot system uses the Python programming language. The message Chatbot flow system will be sent first to the user. Then the Python program will read the incoming message to enter Chatbot. If the incoming message matches the existing conditions, the chatbot will send the information according to the condition. But if it doesn't match, Chatbot will continue to repeat the process of reading incoming messages. The Chatbot system is designed to run successfully on 15 contacts at a time. Chatbot server connection speed affects the speed of sending messages and checking every incoming message. Chatbot simulation program cannot read messages that enter the server if the message contains stickers, emojis and gifs. This is because Python program cannot read the message. This research can still be developed by adding a random message feature.
Smart Home Technologies for Cognitive Assessment in Healthcare

With the term “smart home” developers usually refer to a house, or more generally a residential environment, where a set of integrated sensors, devices and technologies provides the occupants with innovative functionalities and utilities which improve both the living comfort and the resource management of the building.
Sequential Prediction with Logic Constraints for Surgical Robotic Activity Recognition

Many real-world time-sensitive and high-stake applications (e.g., surgical, rescue, and recovery robotics) exhibit sequential nature; thus, applying Recurrent Neural Network (RNN)-based sequential models is an attractive approach to detect robotic activity. One limitation of such approaches is data scarcity. As a result, limited training samples may lead to over-fitting, producing incorrect predictions during deployment. Nevertheless, abundant domain knowledge may still be available, which may help formulate logic constraints. In this paper, we propose a novel way to integrate domain knowledge into RNN-based sequential prediction. We build a Markov Logic Network (MLN)-based classifier that automatically learns constraint weights from data. We propose two methods to incorporate this MLN-based prediction: (i) PriorLayer, in which the values of the hidden layer of the RNN are combined with weights learned from logic constraints in an additional neural network layer, and (ii) Conflation, in which class probabilities from RNN predictions and constraint weights are combined based on the conflation of class probabilities. We evaluate robotic activity classification methods on a simulated OpenAI Gym environment and a real-world DESK dataset for surgical robotics. We observe that our proposed MLN-based approaches boost the performance of LSTM-based networks. In particular, MLN boosts the accuracy of LSTM from 71% to 84% on the Gym dataset and from 68% to 72% on the Taurus robot dataset. Furthermore, MLN (i.e., PriorLayer) shows regularization capability where it improves accuracy in initial LSTM training while avoiding over-fitting early, thus improves the final classification accuracy on unseen data. The code is available at https://github.com/masud99r/prediction-with-logic-constraints.
New technology proposals for tackling intimate partner violence: Challenges and opportunities

Intimate partner violence remains a critical social phenomenon in today's societies. Among the different effective resources and tools to address it, technology seems to offer an innovative procedure to reduce its impact. This article is based on the potential contribution of technology to protect female victims, offering a qualitative analysis of the testimonies of experts who contributed to an ongoing project to design a device capable of providing automatic and immediate warning when women are in at-risk situations. The analysis of the discourses of the experts interviewed provided valuable information to explore the possibilities, limitations and unforeseen effects offered by technology in general, and this new device in particular, for tackling intimate partner violence.
FBNet: FeedBack-Recursive CNN for Saliency Detection

Saliency detection research has achieved great progress with the emergence of convolutional neural network (CNN) in recent years. Most deep learning based saliency models mainly adopt the feed-forward CNN architecture with heavy burden of parameters to learn features via bottom-up manner. However, this forward only process may ignore the intrinsic relationship and potential benefits of top-down connections or information flow. To the best of our knowledge, there is not any work to explore the feedback connection especially in a recursive manner for saliency detection. Therefore, we propose and explore a simple, intuitive yet powerful feedback recursive convolutional model (FBNet) for image saliency detection. Specifically, we first select and define a lightweight baseline feed-forward CNN structure (~4.7MB), then the high-level multi-scale saliency features are fed back to the low-level convolutional blocks in a recursive process. Experimental results show that the feedback recursive process is a promising way to improve the performance of the baseline forward CNN model. Besides, despite having relatively few CNN parameters, the proposed FBNet model achieves competitive results on the public saliency detection benchmarks.
Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Nowadays, the internet of things (IoT) is used to generate data in several application domains. A logistic regression, which is a standard machine learning algorithm with a wide application range, is built on such data. Nevertheless, building a powerful and effective logistic regression model requires large amounts of data. Thus, collaboration between multiple IoT participants has often been the go-to approach. However, privacy concerns and poor data quality are two challenges that threaten the success of such a setting. Several studies have proposed different methods to address the privacy concern but to the best of our knowledge, little attention has been paid towards addressing the poor data quality problems in the multi-party logistic regression model. Thus, in this study, we propose a multi-party privacy-preserving logistic regression framework with poor quality data filtering for IoT data contributors to address both problems. Specifically, we propose a new metric gradient similarity in a distributed setting that we employ to filter out parameters from data contributors with poor quality data. To solve the privacy challenge, we employ homomorphic encryption. Theoretical analysis and experimental evaluations using real-world datasets demonstrate that our proposed framework is privacy-preserving and robust against poor quality data.
How to evaluate blame for gradual types

Programming language theoreticians develop blame assignment systems and prove blame theorems for gradually typed programming languages. Practical implementations of gradual typing almost completely ignore the idea of blame assignment. This contrast raises the question whether blame provides any value to the working programmer and poses the challenge of how to evaluate the effectiveness of blame assignment strategies. This paper contributes (1) the first evaluation method for blame assignment strategies and (2) the results from applying it to three different semantics for gradual typing. These results cast doubt on the theoretical effectiveness of blame in gradual typing. In most scenarios, strategies with imprecise blame assignment are as helpful to a rationally acting programmer as strategies with provably correct blame.