Reviewerly/exHarmony huggingface dataset:
n-BiLSTM: BiLSTM with n-gram Features for Text Classification

Text classification is widely existing in the fields of e-commerce and log message analysis. Besides, it is an essential module in text processing tasks. In this paper, we present a method to create an accurate and fast text classification system in both One-vs.-one and One-vs.-rest manner. Our approach, named n-BiLSTM, is used to convert natural text sentences into features similar to bag-of-words with n-gram techniques, and then the features are fed into a bidirectional LSTM. The two components are able to take better advantages of multi-scale feature representation and context information. Finally, the whole system is evaluated using two labeled movie review datasets, IMDB and SSTb, to test one-vs.-one and one-vs.-rest performances respectively. The results obtained show that our n-BiLSTM algorithm is superior to the basic LSTM and bidirectional LSTM algorithms.
Lightweight Convolutional Neural Network for Real-Time Face Detector on CPU Supporting Interaction of Service Robot

Face detection plays an essential role in the success of the interaction between service robots and consumers. This method is the initial stage for face-related applications. Practical applications require face detection to work in real-time and can be implemented on low-cost devices such as CPU. Traditional methods have problems when the face is not frontal, blocked, and partially covered, but real-time speed is not an obstacle. On the other hand, deep learning has succeeded in accurately distinguishing facial features and backgrounds. Face sizes that tend to be medium and large when robot interaction with consumers so it can employ Convolutional Neural Networks (CNN) with light weights. In this paper, a real-time face detector is built that can work on the CPU. This detector will be implemented explicitly in service robots to support interactions with consumers. It can overcome the occlusion and not-frontal face. Detector architecture consists of the backbone as rapidly features extractor, transition module as a transformer of prediction map, and the dual-detection layer is head of a network prediction based on scale assignment. As a result, the detector can work at speeds of 301 frames per second on CPU without ignoring the accuracy.
Deep Learning Backdoors

In this chapter, we will give a comprehensive survey on backdoor attacks, mitigation and challenges and propose some open problems. We first introduce an attack vector that derives from the Deep Neural Network (DNN) model itself. DNN models are trained from gigantic data that may be poisoned by attackers. Different from the traditional poisoning attacks that interfere with the decision boundary, backdoor attacks create a “shortcut” in the model’s decision boundary. Such a “shortcut” can only be activated by a trigger known by the attacker itself, while it performs well on benign inputs without the trigger. We then show several mitigation techniques from the frontend to the backend of the machine learning pipeline. We finally provide avenues for future research. We hope to raise awareness about the severity of the current emerging backdoor attacks in DNNs and attempt to provide a timely solution to fight against them.
Towards Trusted Data on Decentralized IoT Applications: Integrating Blockchain in Constrained Devices

Blockchain technology is attracting the interest of the Internet of Things (IoT) domain as a decentralized platform for a trusted record of information. Thus, an increasing number of architectures have been proposed for integrating both technologies. However, few of them have focused on the sensing elements of the IoT systems, which are typically low-cost and resource-constrained devices. In this work, we propose an architecture that integrates blockchain at the sensor level, creating a chain of trust from the source of the data. To evaluate our proposal, we develop a cross-platform library for constrained IoT devices and deploy a full-fledged utilities management showcase using off-the-shelf hardware. From a device perspective, we measure the overhead that our architecture introduces in the system, in terms of memory and program space footprint, processing time, and power consumption. From the application perspective, we quantify the additional cost and delay incurred by using a public blockchain. Our results show that even constrained 8-bit devices with a 16-MHz AVR CPU can support the proposed solution. Moreover, the cost of maintaining a transparent record of daily resource consumption on the blockchain can be less than 1 USD a month.
Bias Voltage DAC Operating at Cryogenic Temperatures for Solid-State Qubit Applications

A scalable eight channel DAC designed in a TSMC 65-nm CMOS technology for generation of solid-state quantum bit (qubit) bias voltages is presented. Measurement results of the DAC and some additional auxiliary components like an on-chip amplifier and sigma-delta modulator at 6 K are discussed. With a low-power consumption of 2.7 μW per channel, the DAC fulfills the requirements to be placed next to qubits inside a mixing chamber of a dilution refrigerator, showing a promising way for scaling qubit numbers toward a quantum computer.
Long-Term Action Dependence-Based Hierarchical Deep Association for Multi-Athlete Tracking in Sports Videos

Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, as athletes generally share high similarity in appearance with large deformations. In this paper, unlike the existing hand-crafted solutions, we propose a novel and effective approach to this issue, which hierarchically associates detections of the same identity through discriminative and robust deep features. First, in detection association, we make use of athlete appearances and poses instead of traditional position cues to generate short tracklets for better initialization. Second, in tracklet association, a new deep architecture, namely Siamese Tracklet Affinity Networks (STAN), is presented, which is able to bi-directionally simulate the unseen dynamics of actions, comprehensively models the long-term action dependences, and sequentially estimates their affinity. Such hierarchical association is finally solved as a minimum-cost network flow problem. We extensively evaluate the proposed approach on the APIDIS, NCAA Basketball and VolleyTrack (newly collected) databases, and the experimental results show its advantages.
Multiview Variational Sparse Gaussian Processes

Gaussian process (GP) models are flexible nonparametric models widely used in a variety of tasks. Variational sparse GP (VSGP) scales GP models to large data sets by summarizing the posterior process with a set of inducing points. In this article, we extend VSGP to handle multiview data. We model each view with a VSGP and augment it with an additional set of inducing points. These VSGPs are coupled together by enforcing the means of their posteriors to agree at the locations of these inducing points. To learn these shared inducing points, we introduce an additional GP model that is defined in the concatenated feature space. Experiments on real-world data sets show that our multiview VSGP (MVSGP) model outperforms single-view VSGP consistently and is superior to state-of-the-art kernel-based multiview baselines for classification tasks.
Few-shot Object Detection and Viewpoint Estimation for Objects in the Wild

Detecting objects and estimating their viewpoints in images are key tasks of 3D scene understanding. Recent approaches have achieved excellent results on very large benchmarks for object detection and viewpoint estimation. However, performances are still lagging behind for novel object categories with few samples. In this paper, we tackle the problems of few-shot object detection and few-shot viewpoint estimation. We demonstrate on both tasks the benefits of guiding the network prediction with class-representative features extracted from data in different modalities: image patches for object detection, and aligned 3D models for viewpoint estimation. Despite its simplicity, our method outperforms state-of-the-art methods by a large margin on a range of datasets, including PASCAL and COCO for few-shot object detection, and Pascal3D+ and ObjectNet3D for few-shot viewpoint estimation. Furthermore, when the 3D model is not available, we introduce a simple category-agnostic viewpoint estimation method by exploiting geometrical similarities and consistent pose labeling across different classes. While it moderately reduces performance, this approach still obtains better results than previous methods in this setting. Last, for the first time, we tackle the combination of both few-shot tasks, on three challenging benchmarks for viewpoint estimation in the wild, ObjectNet3D, Pascal3D+ and Pix3D, showing very promising results.
Intelligent Malicious URL Detection with Feature Analysis

The website security is an important issue that must be pursued to protect Internet users. Traditionally, blacklists of malicious websites are maintained, but they do not help in the detection of new malicious websites. This work proposes a machine learning architecture for intelligent detecting malicious URLs. Forty-one features of malicious URLs are extracted from the data processes of domain, Alexa and obfuscation. ANOVA (Analysis of Variance) test and XGBoost (eXtreme Gradient Boosting) algorithm are used to identify the 17 most important features. Finally, dataset is used to learn the XGBoost classifier, which has a detection accuracy of more than 99%.
Efficiently mining erasable stream patterns for intelligent systems over uncertain data

Data mining is a method for extracting useful information that is necessary for a system from a database. As the types of data processed by the system are diversified, the transformed pattern mining techniques for processing these type of data have been proposed. Unlike the traditional pattern mining methods, erasable pattern mining is a technique for finding the patterns that can be removed by coming with a small profit. Erasable pattern mining should be able to process data by considering both the environment that the data are generated from and the characteristics of the data. An uncertain database is a database that is composed of uncertain data. Since erasable patterns discovered from uncertain data contain significant information, these patterns need to be extracted. In addition, databases gradually increase, because the data from various fields is generated and accumulated over data streams. Data streams should be processed as intelligently as possible to provide the useful data to the system in real time. In this paper, we propose an efficient erasable pattern mining algorithm that processes uncertain data that is generated over data streams. The uncertain erasable patterns discovered through the suggested technique are more meaningful information by considering the probability of the item and the profit. Moreover, the proposed method can perform efficient mining operations by using both tree and list structures. The performance of the suggested algorithm is verified through the performance tests compared with state-of-the-art algorithms using real data sets and synthetic data sets.
Improving approximate neural networks for perception tasks through specialized optimization

Approximate Computing has been proven successful in reducing the energy consumption of Deep Neural Networks (DNNs) implemented in embedded systems. For efficient DNN approximation at software and hardware levels, a specialized simulation environment and optimization methodology are required, to reduce execution and optimization times, as well as to maximize energy savings. Traditional frameworks for cross-layer approximate computation of DNNs are generally built only for simulation of convolutional and fully-connected layers, limiting the DNN types to be optimized through approximations. In this work, we present a specialized simulation environment for approximate DNNs, which allows for optimization of several DNN architectures built with more complex DNN layers such as depthwise convolutions and Recurrent Neural Units (RNNs) for time series processing. Low execution time overhead is achieved hereby through efficient GPU acceleration. Additionally, we deliver an analysis of approximate DNN and RNN robustness against quantization noise and different approximation levels. Finally, through specialized approximate retraining, we achieve promising energy savings and negligible accuracy losses with highly complex DNNs for image classification with ImageNet, such as MobileNet, and RNNs for keyword spotting with the Speech Commands Dataset.
Object-oriented detection of building shadow in TripleSat-2 remote sensing imagery

The projection of objects on the earth's surface caused by the sunlight produces shadows. They are inevitable in high-spatial-resolution satellite remote sensing images and reduce the accuracy of change detection, land cover classification, target recognition, and many more applications. Dark-colored land covers in these satellite images, such as water bodies, road, and soil, appear with similar spectral properties as those of shadows and often result in difficulty in shadow detection, especially in complex urban settings. We propose an object-oriented building shadow extraction method and tested it using six selected study areas from TripleSat-2 satellite imagery with 3.2-m spatial resolution. The method's main steps include (1) selecting six image features that can highlight the shadow information and then segment the image based on edge; (2) extracting shadow region based on multiple object features; and (3) masking nonbuilding shadow regions by the shadow and dark object separation index, image features including spectral, textural, and geometric features, and contextual information. The average precision, recall, and F1-score of the shadow detection were 85.6%, 88.6%, and 87.0%, respectively, and the ranges were 73.0% to 91.0%, 76.6% to 94.1%, and 74.7% to 91.2%, respectively. Compared with multiscale segmentation, edge-based segmentation is more efficient and helpful to completely and accurately extract shadows.
Government ideology and economic freedom

This paper tests the widespread belief that right-wing governments tend to promote economic freedom while left-wing ones prefer more control over the economy. Using annual data for 106 countries over the period 1975-2015 and a two-step system GMM estimator, this study shows that right-wing governments are indeed more prone to promote economic freedom and to deregulate the economy. It also finds that this effect seems to be stronger in developing/emerging economies than in more developed countries. Moreover, the analysis of the broad dimensions of economic freedom indicates that right-wing governments tend to act by improving monetary soundness and freedom to trade internationally and by easing regulations that restrict entry into markets. Mainstream left-wing governments do not seem to affect economic freedom, however populist left-wing parties, when in office, are found to have a detrimental effect.
Reversible Data Hiding in Encrypted Images using Chunk Encryption and Redundancy Matrix Representation

For reversible data hiding in encrypted images (RDHEI), the private information in the original image content is protected and the embedded secret data can be used to manage the encrypted image. Up to now, many RDHEI algorithms have been reported, however, these reported RDHEI algorithms cannot achieve a high embedding capacity. Therefore, this article proposes a new RDHEI scheme, which will have improved performance. The main contributions of the proposed scheme include two points: chunk encryption (CE) and redundancy matrix representation (RMR). First, different from the encryption method of pixel-by-pixel encryption used in the previous RDHEI algorithms, CE is the method that uses the traditional exclusive-or (XOR) method to encrypt the original image in chunks. By using the CE method, redundancy will be retained in the encrypted chunks. Second, the RMR method is designed to generate available room for accommodating secret data in redundancy matrices, which are existed in some encrypted chunks. Experimental results show that the proposed scheme achieves a high embedding capacity and the directly decrypted images retain high quality.
A Standalone RFID-Based Mobile Robot Navigation Method Using Single Passive Tag

This article proposes a standalone radio frequency identification (RFID)-based mobile robot navigation method, in which a mobile robot equipped with reader antennas can be continuously guided to a static object marked with a single passive UHF RFID tag. An observation model based on the RFID phase difference is built and integrated into a particle filter, by which the instantaneous relative position between the mobile robot and the tagged object can be detected in real time. Based on the position information extracted from the RFID system, the mobile robot adjusts its pose to move toward the RFID-tagged object. Compared with the existing RFID-based mobile robot navigation methods, the proposed method requires no external sensors other than the RFID and requires only a single passive tag. Experiments using commercial off-the-shelf (COTS) RFID devices are performed, and the results indicate that the mobile robot can satisfactorily realize navigation task with a distance accuracy of 4.04 cm and a bearing accuracy of 2.23°. The proposed method is well applicable for the navigation scenes in which the absolute position of the tagged target object is not known beforehand. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —UHF radio frequency identification (RFID) has been widely applied as an asset management ID sensor in many fields. RFID-based mobile robot navigation technology can further increase its application value as a location sensor. This article proposes a standalone RFID-based mobile robot navigation method, in which the reference tag and the external sensors other than RFID are both not required. In the proposed method, only a single passive tag is attached to the static target object. Experimental results indicate that the proposed method can enable navigation task with good performance in situations in which the absolute navigation goal position is not known in advance.
Monitoring Menses: Design-Based Investigations of Menstrual Tracking Applications

Abstract Fox and Epstein interrogate and reimagine menstrual tracking technology, focusing on mobile applications designed to document and quantify menstrual cycle data. While such technology promises to provide users with new insights and predictions, the authors highlight how these apps are inscribed with particular visions of menstruation and encourage users to extract intimate information about their bodies. Apps regularly assume, for instance, that all possible users are women, heterosexual, and monogamous, have a “normal” cycle, and use tracking techniques exclusively to gauge fertility. Fox and Epstein present two case studies: (1) examining core issues of usability and inclusion in the design of existing applications; and (2) using participatory approaches to highlight the lived experiences of menstruators and introduce alternatives to dominant menstrual app protocols. In offering this two-part analysis, they point to important openings for exploring how these technologies might support the needs of a multiplicity of menstruating bodies.
Social Attentive Deep Q-Networks for Recommender Systems

Recommender systems aim to accurately and actively provide users with potentially interesting items (products, information or services). Deep reinforcement learning has been successfully applied to recommender systems, but still heavily suffer from data sparsity and cold-start in real-world tasks. In this work, we propose an effective way to address such issues by leveraging the pervasive social networks among users in the estimation of action-values (Q). Specifically, we develop a Social Attentive Deep Q-network (SADQN) to approximate the optimal action-value function based on the preferences of both individual users and social neighbors, by successfully utilizing a social attention layer to model the influence between them. Further, we propose an enhanced variant of SADQN, termed SADQN++, to model the complicated and diverse trade-offs between personal preferences and social influence for all involved users, making the agent more powerful and flexible in learning the optimal policies. The experimental results on real-world datasets demonstrate that the proposed SADQNs remarkably outperform the state-of-the-art deep reinforcement learning agents, with reasonable computation cost.
GRIP: A Graph Neural Network Accelerator Architecture

We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. Accelerating GNNs is challenging because they combine two distinct types of computation: arithmetic-intensive <i>vertex-centric</i> operations and memory-intensive <i>edge-centric</i> operations. GRIP splits GNN inference into a three edge- and vertex-centric execution phases that can be implemented in hardware. GRIP specializes each unit for the unique computational structure found in each phase. For vertex-centric phases, GRIP uses a high performance matrix multiply engine coupled with a dedicated memory subsystem for weights to improve reuse. For edge-centric phases, GRIP use multiple parallel prefetch and reduction engines to alleviate the irregularity in memory accesses. Finally, GRIP supports several GNN optimizations, including an optimization called vertex-tiling that increases the reuse of weight data. We evaluate GRIP by performing synthesis and place and route for a <inline-formula><tex-math notation="LaTeX">$28 \;\mathrm{n}\mathrm{m}$</tex-math></inline-formula> implementation capable of executing inference for several widely-used GNN models (GCN, GraphSAGE, G-GCN, and GIN). Across several benchmark graphs, it reduces 99th percentile latency by a geometric mean of <inline-formula><tex-math notation="LaTeX">$17\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$23\times$</tex-math></inline-formula> compared to a CPU and GPU baseline, respectively, while drawing only <inline-formula><tex-math notation="LaTeX">$5 \;\mathrm{W}$</tex-math></inline-formula> .
Class Attendance Management System using Facial Recognition

Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However,these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.