Reviewerly/exHarmony huggingface dataset:
Glowworm Attack: Optical TEMPEST Sound Recovery via a Device's Power Indicator LED

Two main classes of optical TEMPEST attacks against the confidentiality of information processed/delivered by devices have been demonstrated in the past two decades; the first class includes methods for recovering content from monitors, and the second class includes methods for recovering keystrokes from physical and virtual keyboards. In this paper, we identify a new class of optical TEMPEST attacks: recovering sound by analyzing optical emanations from a device's power indicator LED. We analyze the response of the power indicator LED of various devices to sound and show that there is an optical correlation between the sound that is played by connected speakers and the intensity of their power indicator LED due to the facts that: (1) the power indicator LED of various devices is connected directly to the power line, (2) the intensity of a device's power indicator LED is correlative to the power consumption, and (3) many devices lack a dedicated means of countering this phenomenon. Based on our findings, we present the Glowworm attack, an optical TEMPEST attack that can be used by eavesdroppers to recover sound by analyzing optical measurements obtained via an electro-optical sensor directed at the power indicator LED of various devices (e.g., speakers, USB hub splitters, and microcontrollers). We propose an optical-audio transformation (OAT) to recover sound in which we isolate the speech from optical measurements obtained by directing an electro-optical sensor at a device's power indicator LED. Finally, we test the performance of the Glowworm attack in various experimental setups and show that an eavesdropper can apply the attack to recover speech from speakers' power LED indicator with good intelligibility from a distance of 15 meters and with fair intelligibility from 35 meters.
Robust Unsupervised Feature Selection via Multi-Group Adaptive Graph Representation

Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method, referred to as Multi-Group Adaptive Graph Representation (MGAGR). Different from existing methods, the relationship between features is explored via the global similarity matrix, which is reconstructed by local similarities of multiple groups. Specifically, the similarity of a feature compared to other features can be represented by the linear combination of all the local similarities. The local similarity of a representative group is given a large weight to reconstruct the global similarity. Besides, an iterative algorithm is given to solve the optimization problem, in which the global similarity matrix, its corresponding reconstruction weights and the self-representation matrix are iteratively improved. Experimental results on 8 benchmark datasets demonstrates that the proposed method outperforms the state-of-the-art unsupervised feature selection methods in terms of clustering performance. The source code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/misteru/MGAGR</uri> .
Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning

Active tracking control is essential for UAVs to perform autonomous operations in GPS-denied environments. In the active tracking task, UAVs take high-dimensional raw images as input and execute motor actions to actively follow the dynamic target. Most research focuses on three-stage methods, which entail perception first, followed by high-level decision-making based on extracted spatial information of the dynamic target, and then UAV movement control, using a low-level dynamic controller. Perception methods based on deep neural networks are powerful but require considerable effort for manual ground truth labeling. Instead, we unify the perception and decision-making stages using a high-level controller and then leverage deep reinforcement learning to learn the mapping from raw images to the high-level action commands in the V-REP-based environment, where simulation data are infinite and inexpensive. This end-to-end method also has the advantages of a small parameter size and reduced effort requirements for parameter turning in the decision-making stage. The high-level controller, which has a novel architecture, explicitly encodes the spatial and temporal features of the dynamic target. Auxiliary segmentation and motion-in-depth losses are introduced to generate denser training signals for the high-level controller’s fast and stable training. The high-level controller and a conventional low-level PID controller constitute our hierarchical active tracking control framework for the UAVs’ active tracking task. Simulation experiments show that our controller trained with several augmentation techniques sufficiently generalizes dynamic targets with random appearances and velocities, and achieves significantly better performance, compared with three-stage methods.
APECS: A Distributed Access Control Framework for Pervasive Edge Computing Services

Edge Computing is a new computing paradigm where applications operate at the network edge, providing low-latency services with augmented user and data privacy. A desirable goal for edge computing is pervasiveness, that is, enabling any capable and authorized entity at the edge to provide desired edge services--pervasive edge computing (PEC). However, efficient access control of users receiving services and edge servers handling user data, without sacrificing performance is a challenge. Current solutions, based on "always-on" authentication servers in the cloud, negate the latency benefits of services at the edge and also do not preserve user and data privacy. In this paper, we present APECS, an advanced access control framework for PEC, which allows legitimate users to utilize any available edge services without need for communication beyond the network edge. The APECS framework leverages multi-authority attribute-based encryption to create a federated authority, which delegates the authentication and authorization tasks to semi-trusted edge servers, thus eliminating the need for an "always-on" authentication server in the cloud. Additionally, APECS prevents access to encrypted content by unauthorized edge servers. We analyze and prove the security of APECS in the Universal Composability framework and provide experimental results on the GENI testbed to demonstrate the scalability and effectiveness of APECS.
Sparse attention block: Aggregating contextual information for object detection

It is well recognized that the contextual information of surrounding objects is beneficial for object detection. Such contextual information can often be obtained from long-range dependencies. This paper proposes a sparse attention block to capture long-range dependencies in an efficient way. Unlike the conventional non-local block, which generates a dense attention map to characterize the dependency between any two positions of the input feature map, our sparse attention block samples the most representative positions for contextual information aggregation. After searching for local peaks in a heat map of the given input feature map, it adaptively selects a sparse set of positions to represent the relationship between query and key elements. With the obtained sparse positions, our sparse attention block can well model long-range dependencies, and greatly improve the object detection performance at the additional cost of <2% GPU memory and computation of the conventional non-local block. This sparse attention block can be easily plugged into various object detection frameworks, such as Faster R-CNN, RetinaNet and Mask R-CNN. Experiments on COCO benchmark confirm that our sparse attention block can boost the detection accuracy with significant gains ranging from 1.4% to 1.9% and negligible overhead of computation and memory usage.
GAN-Based Anomaly Detection for Multivariate Time Series Using Polluted Training Set

Multivariate time series anomaly detection has great potentials in many practical applications. Extreme unbalanced training set and noise interference make it challenging to accurately capture the distribution of normal data and then detect anomalies. Existing AutoEncoder(AE)-based approaches are lack of effective regularization method specially designed for anomaly detection tasks thus easily overfitting while Generative Adversarial Network(GAN)-based approaches are mostly trained under the hypothesis of pollution-free training set, which means the training set is all composed of normal samples and that is hard to satisfy in practice. To tackle these problems, in this paper we propose a GAN based anomaly detection method for multivariate time series named FGANomaly (letter F is for Filter). The core idea is to filter possible anomalous samples with pseudo-labels before training the discriminator thus to capture the distribution of normal data as precise as possible. In addition, we design a novel training objective for the generator, which leads the generator to concentrate more on plausible normal data and ignore anomalies. We conducted comprehensive experiments on four public datasets, and the experimental results show the superiority of our method over baselines in both performance and robustness.
ICFinder

Chemical materials are useful but sometimes hazardous, which requires strict regulation from the government. However, due to the potential economic benefits, many illegal hazardous chemical facilities are running underground, which poses a significant public safety threat. However, the traditional solutions, e.g., on-field screening and the anonymous tip-offs, involve a lot of human efforts. In this paper, we propose a ubiquitous approach called ICFinder to detecting illegal chemical facilities with chemical transportation trajectories. We first generate candidate locations by clustering stay points extracted from trajectories, and filter out known locations. Then, we rank those locations in suspicion order by modeling whether it has the loading/unloading events. ICFinder is evaluated over the real-world dataset from Nantong in China, and the deployed system identified 20 illegal chemical facilities in 3 months.
Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation

Cellularity estimation forms an important aspect of the visual examination of bone marrow biopsies. In clinical practice, cellularity is estimated by eye under a microscope, which is rapid, but subjective and subject to inter- and intraobserver variability. In addition, there is little consensus in the literature on the normal variation of cellularity with age. Digital image analysis may be used for more objective quantification of cellularity. As such, we developed a deep neural network for the segmentation of six major cell and tissue types in digitized bone marrow trephine biopsies. Using this segmentation, we calculated the overall bone marrow cellularity in a series of biopsies from 130 patients across a wide age range. Using intraclass correlation coefficients (ICC), we measured the agreement between the quantification by the neural network and visual estimation by two pathologists and compared it to baseline human performance. We also examined the age-related changes of cellularity and cell lineages in bone marrow and compared our results to those found in the literature. The network was capable of accurate segmentation (average accuracy and dice score of 0.95 and 0.76, respectively). There was good neural network-pathologist agreement on cellularity measurements (ICC=0.78, 95% CI 0.58-0.85). We found a statistically significant downward trend for cellularity, myelopoiesis and megakaryocytes with age in our cohort. The mean cellularity began at approximately 50% in the third decade of life and then decreased ±2% per decade to 40% in the seventh and eighth decade, but the normal range was very wide (30-70%).
Predicting Success in the Embryology Lab: The Use of Algorithmic Technologies in Knowledge Production

This article analyzes local algorithmic practices resulting from the increased use of time-lapse (TL) imaging in fertility treatment. The data produced by TL technologies are expected to help professionals pick the best embryo for implantation. The emergence of TL has been characterized by promissory discourses of deeper embryo knowledge and expanded selection standardization, despite professionals having no conclusive evidence that TL improves pregnancy rates. Our research explores the use of TL tools in embryology labs. We pay special attention to standardization efforts and knowledge-creation facilitated through TL and its incorporated algorithms. Using ethnographic data from five UK clinical sites, we argue that knowledge generated through TL is contingent upon complex human-machine interactions that produce local uncertainties. Thus, algorithms do not simply add medical knowledge. Rather, they rearrange professional practice and expertise. Firstly, we show how TL changes lab routines and training needs. Secondly, we show that the human input TL requires renders the algorithm itself an uncertain and situated practice. This, in turn, raises professional questions about the algorithm's authority in embryo selection. The article demonstrates the embedded nature of algorithmic knowledge production, thus pointing to the need for STS scholarship to further explore the locality of algorithms and AI.
Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks

Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy.Sparse CNNs, however, present their own set of challenges including non-linear data accesses and complex design of CNN processing elements (PEs).Recently proposed accelerators like SCNN, Eyeriss v2, and SparTen, exploit the two-sided sparsity, that is, sparsity in both the input activations and weights to accelerate the CNN inference.These, accelerators, however, suffer from a multitude of problems that limit their applicability, such as inefficient micro-architecture (SCNN, Eyeriss v2), complex PE design (Eyeriss v2), no support for non-unit stride convolutions (SCNN) and FC layers (SparTen, SCNN).To address these issues in contemporary sparse CNN accelerators, we propose Sparse-PE, a multi-threaded, and flexible CNN PE, capable of handling both the dense and sparse CNNs.The Sparse-PE core uses binary mask representation and actively skips computations involving zeros and favors non-zero computations, thereby, drastically increasing the effective throughput and hardware utilization.Unlike previous designs, the Sparse-PE core is generic in nature and not targeted towards a specific accelerator, and thus, can also be used as a standalone sparse dot product compute engine.We evaluate the performance of the core using a custom built cycle accurate simulator.Our simulations show that the Sparse-PE core-based accelerator provides a performance gain of 12× over a recently proposed dense accelerator (NeuroMAX).For sparse accelerators, it provides a performance gain of 4.2×, 2.38×, and 1.98× over SCNN, Eyeriss v2, and SparTen, respectively.
Emotion recognition: A smoothed Dirichlet multinomial solution

Multinomial-based models have been extensively used for count data modeling and challenging applications such as image processing, text recognition, and behavioral sciences. Despite the good performance obtained with those models, they still suffer from challenging issues that require continuous exploring of other alternative approaches. In this work, we address the issue of smoothing language modeling. To the best of our knowledge, distributions defined in a smoothed simplex were not considered before as conjugate priors for the multinomial. We propose a smoothed Dirichlet multinomial (SDM) distribution and a mixture of SDMs with a likelihood-based learning. We evaluate the proposed approach on three challenging applications related to emotion recognition: depression on social media, happiness analysis, and pain estimation. The smoothed Dirichlet multinomial solution presents the best results comparing to the related works and the multinomial-based models such as Dirichlet compound multinomial and the multinomial model.
Image Caption Generator Using Attention Mechanism

Image captioning is used to generate sentences describing the scene captured in the form of images. It identifies objects in the image, performs a few operations, and tries to find the salient features of the image. Once the system identifies this information, it should further generate the most relevant and brief description for the image, which should be both syntactically and semantically correct. With the advancements of Learning techniques, algorithms can generate text in the form of natural sentences that will be able to describe an image in its best form. The natural ability of humans to understand image content and generate descriptive text is a challenging task for a machine to imitate. The applications of image caption generation are extensive and significant. The task involves generating brief captions using various techniques like Natural language processing (NLP), Computer vision (CV), and Deep Learning (DL) techniques. This paper introduces a system that uses an attention mechanism alongside an encoder and a decoder to generate the captions. It uses a pre-trained Convolutional Neural Network (CNN) viz. Inception V3 to extract features of the image and then a Recurrent Neural Network (RNN) viz. GRU to generate a relevant caption. To generate captions, the proposed model uses an attention mechanism that is Bahdanau attention. MS-COCO dataset is used to train the model. The results validate the model's reasonable ability to understand images and generate text.
Cervical lesion image enhancement based on conditional entropy generative adversarial network framework

Early screening and diagnosis of cervical precancerous lesions are very important to prevent cervical cancer. High-quality colposcopy images will help doctors make faster and more accurate diagnoses. To tackle the problem of low image quality caused by complex interference during colposcopy operation, this paper proposed a conditional entropy generative adversarial networks framework for image enhancement. A decomposition network based on Retinex theory is constructed to obtain the reflection images of the low-quality images, then the conditional generative adversarial network is used as the enhancement network. The low-quality images and the decomposed reflection images are both input the enhancement network for training, and the conditional entropy distance is used as a part of the loss of the conditional generative adversarial network to alleviate the over-fitting problem during the training process. The test results show that compared with published methods, the proposed method of this paper can significantly improve the image quality, and can enhance the colposcopy image while retaining image details.
Building the World of Internet of Things

When technology started growing, there was a vision of a world, by many leading technologists, that objects of all kinds would be behaving and interacting like humans. They would be able to analyse, react, sense and communicate with each other as well as humans in exactly the same way as humans do. All this done by gathering data. Any action reveals a set of data about it and using that data one can predict and modify future acts. This is a very generic way of explaining the phenomenon. The process is however, quite complicated. There are a whole set of technologies which allow the gathering of data and creating information. The "thing" in the internet of things may have several instances, such as a human being with a pace maker, a wildlife animal with a biochip transponder, a built-in vehicle or some other natural or man-made object that can be given an Internet Protocol (IP) address that can transfer data over a network. IOT has an impact on various aspects of businesses like analytics, security, customer information etc. Today, most businesses are trying to integrate IoT enabled capabilities into their strategies. But before that a general understanding of the technologies is very important. Communication in Organisations is done through networks which can be said to be done through electronic signals. An example of this would be the wireless communication mechanism that exists and allows for large dataflow. Sensors are also devices which "sense" certain conditions like in cars, indicators are there for fuel level, battery, temperature as well as closeness of an object. These are one example. Sensors are now seen in many devices like air conditioners, heaters, lighting etc. Analytical instruments are used by organisations to enhance their capacity to describe, predict and take advantage of certain connections or links. These methods are said to be using augmented technology. When procedures are taken in a prescribed way of action with the intervention of humans using technology and methods according to prescribed action, it is known as augmented behaviour. Machine-to-machine interfaces delete human faults in a reliable manner. There are also certain commonly prescribed norms for action known as standards. These standards enable compatibility with different platform using IOT. This chapter will give an insight to all the underlying technologies of IOT. Starting from Networks to sensors, standards, Augmented Intelligence, Augmented behaviour.
Investigating the exceptionality of scattered polynomials

Scattered polynomials over a finite field Fqn have been introduced by Sheekey in 2016, and a central open problem regards the classification of those that are exceptional. So far, only two families of exceptional scattered polynomials are known. Very recently, Longobardi and Zanella weakened the property of being scattered by introducing the notion of L-qt-partially scattered and R-qt-partially scattered polynomials, for t a divisor of n. Indeed, a polynomial is scattered if and only if it is both L-qt-partially scattered and R-qt-partially scattered. In this paper, by using techniques from algebraic geometry over finite fields and function fields theory, we show that the property which is the hardest to be preserved is the L-qt-partially scattered one. We investigate a large family F of R-qt-partially scattered polynomials, containing examples of exceptional R-qt-partially scattered polynomials, which turn out to be connected with linear sets of so-called pseudoregulus type. We introduce two different notions of equivalence preserving the property of being R-qt-partially scattered. Many polynomials in F are inequivalent and geometric arguments are used to determine their equivalence classes under the action of ΓL(2n/t,qt).