Reviewerly/exHarmony huggingface dataset:
‘Unmochon’: A Tool to Combat Online Sexual Harassment over Facebook Messenger

Women in the global south often seek justice to their online harassment through unveiling the harassers and the screenshots of their sent harassment texts and visual contents before the relevant authorities. Nevertheless, such evidence is often challenged for their authenticity. Our survey (n=91) and interview (n=43) with Bangladeshi online gender harassment victims revealed the depth of the problem, and we set design goals to collect evidence from Facebook Messenger with ensured authenticity. Building on the ‘shame-based model’ of gender justice [12], we designed ‘Unmochon’, a tool that captures authentic evidence and shares with victims’ intended group. Our user-study (n=48) revealed that diminishing authenticity problem may still leave the victim and online gender justice entangled with mob-sentiment, hegemonic legal consciousness, and several privacy aspects. Our findings open up a new discussion on how HCI-design should address online gender justice in such a complex social setting.
“That courage to encourage”: Participation and Aspirations in Chat-based Peer Support for Youth Living with HIV

We present a qualitative study of a six-month pilot of WhatsApp-based facilitated peer support groups, serving youth living with human immunodeficiency virus (HIV) in an informal settlement in Nairobi, Kenya. Popular chat apps are increasingly being leveraged to make a combination of patient-provider communication and peer support more accessible beyond formal healthcare settings. However, how these interventions are experienced in Global South contexts with phone sharing and intermittent data access is understudied. The context of stigmatized illnesses like HIV further complicates privacy concerns. We draw on chat records and interviews with youth and the facilitator to describe their experience of the intervention. We find that despite tensions in group dynamics, intermittent participation, and contingencies around privacy, youth were motivated by newfound aspirations and community to manage their health. We use our findings to discuss implications for the design of chat-based peer interventions, negotiation of privacy in mobile health applications, and the role of aspirations in health interventions.
Intelligent Denoising-Aided Deep Learning Modulation Recognition With Cyclic Spectrum Features for Higher Accuracy

Deep-learning-based modulation recognition methods can extract the features of signals automatically with the usage of the deep neural network (DNN). However, the background noises might lower the recognition accuracy and induce longer convergence time. In order to improve the recognition accuracy and to reduce the computational complexity, in this article, we propose to construct the dataset based on spectral correlation function (SCF), which has the special property of relatively being insensitive to background noises. Moreover, we propose to add a convolutional neural network denoising module to combat the noises for better feature extraction performances. Furthermore, in order to reduce the dataset size, we propose to use the two-dimensional (2-D) cross-section SCF profiles for the DNN. Then, we propose a DNN architecture based on the back-propagation neural network. Thanks to the noise resistant SCF feature extractions and the intelligent denoising as well as DNN-based classification design, the recognition accuracy can be improved. Simulation results show that the proposed method can effectively classify modulation schemes at the signal-to-noise ratio (SNR) as low as $-$10 dB. Moreover, the proposed intelligent system achieves higher recognition accuracy than counterpart schemes in low-SNR regions.
Machine Learning Approach to Real-Time 3D Path Planning for Autonomous Navigation of Unmanned Aerial Vehicle

The need for civilian use of Unmanned Aerial Vehicles (UAVs) has drastically increased in recent years. Their potential applications for civilian use include door-to-door package delivery, law enforcement, first aid, and emergency services in urban areas, which put the UAVs into obstacle collision risk. Therefore, UAVs are required to be equipped with sensors so as to acquire Artificial Intelligence (AI) to avoid potential risks during mission execution. The AI comes with intensive training of an on-board machine that is responsible to autonomously navigate the UAV. The training enables the UAV to develop humanoid perception of the environment it is to be navigating in. During the mission, this perception detects and localizes objects in the environment. It is based on this AI that this work proposes a real-time three-dimensional (3D) path planner that maneuvers the UAV towards destination through obstacle-free path. The proposed path planner has a heuristic sense of A⋆ algorithm, but requires no frontier nodes to be stored in a memory unlike A⋆. The planner relies on relative locations of detected objects (obstacles) and determines collision-free paths. This path planner is light-weight and hence a fast guidance method for real-time purposes. Its performance efficiency is proved through rigorous Software-In-The-Loop (SITL) simulations in constrained-environment and preliminary real flight tests.
A Lightweight Deep Network for Efficient CSI Feedback in Massive MIMO Systems

To fully exploit the advantages of massive multiple-input multiple-output (m-MIMO), accurate channel state information (CSI) is required at the transmitter. However, excessive CSI feedback for large antenna arrays is inefficient and thus undesirable in practical applications. By exploiting the inherent correlation characteristics of complex-valued channel responses in the angular-delay domain, we propose a novel neural network (NN) architecture, namely ENet, for CSI compression and feedback in m-MIMO. Even if the ENet processes the real and imaginary parts of the CSI values separately, its special structure enables the network trained for the real part only to be reused for the imaginary part. The proposed ENet shows enhanced performance with the network size reduced by nearly an order of magnitude compared to the existing NN-based solutions. Experimental results verify the effectiveness of the proposed ENet.
Bidirectional Gated Temporal Convolution with Attention for text classification

In text classification models based on deep learning, feature extraction and feature aggregation are two key steps. As one of the basic feature extraction methods, CNN has certain limitations due to its inability to effectively extract temporal features from text data. Using max-pooling can significantly reduce the amount of calculation while performing feature aggregation, but it will have an adverse effect on the classification results due to the loss of some text features. In this paper, in response to the above two issues, a Bidirectional Gated Temporal Convolutional Attention(BG-TCA) model is proposed. In the feature extraction stage, the BG-TCA model uses the bidirectional TCN to extract the bidirectional temporal features in text data, and a gating mechanism similar to the LSTM is added between the convolution layers. In the feature aggregation stage, the BG-TCA model uses the attention mechanism to replace the max-pooling method, which makes it possible to distinguish the importance of different features while retaining the text features to the maximum. Finally, experimental results on five benchmark datasets show that the classification accuracy of the BG-TCA model has been greatly improved compared to basic models, and is better than several other state-of-the-art models.
Fast Support Vector Classification for Large-Scale Problems

The support vector machine (SVM) is a very important machine learning algorithm with state-of-the-art performance on many classification problems. However, on large datasets it is very slow and requires much memory. To solve this defficiency, we propose the fast support vector classifier (FSVC) that includes: 1) an efficient closed-form training free of any numerical iterative procedure; 2) a small collection of class prototypes that avoids to store in memory an excessive number of support vectors; and 3) a fast method that selects the spread of the radial basis function kernel directly from data, without classifier execution nor iterative hyper-parameter tuning. The memory requirements of FSVC are very low, spending in average only 6 ·10-7 sec. per pattern, input and class, and processing datasets up to 31 millions of patterns, 30,000 inputs and 131 classes in less than 1.5 hours (less than 3 hours with only 2GB of RAM). In average, the FSVC is 10 times faster, requires 12 times less memory and achieves 4.7 percent more performance than Liblinear, that fails on the 4 largest datasets by lack of memory, being 100 times faster and achieving only 6.7 percent less performance than Libsvm. The time spent by FSVC only depends on the dataset size and thus it can be accurately estimated for new datasets, while Libsvm or Liblinear are much slower on "difficult" datasets, even if they are small. The FSVC adjusts its requirements to the available memory, classifying large datasets in computers with limited memory. Code for the proposed algorithm in the Octave scientific programming language is provided.1.
What Can We Learn about Science Teachers’ Technology Use during the COVID-19 Pandemic?

The purpose of this qualitative research was to describe teachers’ experiences in a technology-mediated teaching context during the COVID-19 pandemic. We mainly focused on teachers’ experiences with technology use (change and variety of the use) and their willingness to use technology in teaching. We designed an interview-based study. The participants were Estonian science teachers who voluntarily agreed to share their experiences about teaching in new and—for most of them—unexpected, distanced learning conditions. Based on teachers’ reflections on technology use we could distinguish between three groups in which teachers described different levels of willingness to use technology, change in technology use from pre-COVID to distanced learning, and variety in the use of technology. Our results revealed that the higher teachers’ perceived willingness to use technology, the easier it was for them to overcome potential obstacles and cope with the unexpected distanced learning. The main obstacles there were grouped as external (e.g., issues with internet connection, lack of students’ digital skills) and internal (e.g., teachers’ beliefs about technology use for teaching). I was observed that some obstacles were shared by all teachers (such as limitations on students’ digital skills) whereas others where more prevalent in separate groups. This highlights the importance of understanding and considering the variability in the possible obstacles that emerge in using technology in education for teachers with different levels of experience.
Meta Self-training for Few-shot Neural Sequence Labeling

Neural sequence labeling is widely adopted for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER) and slot tagging for dialog systems and semantic parsing. Recent advances with large-scale pre-trained language models have shown remarkable success in these tasks when fine-tuned on large amounts of task-specific labeled data. However, obtaining such large-scale labeled training data is not only costly, but also may not be feasible in many sensitive user applications due to data access and privacy constraints. This is exacerbated for sequence labeling tasks requiring such annotations at token-level. In this work, we develop techniques to address the label scarcity challenge for neural sequence labeling models. Specifically, we propose a meta self-training framework which leverages very few manually annotated labels for training neural sequence models. While self-training serves as an effective mechanism to learn from large amounts of unlabeled data via iterative knowledge exchange -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels. Extensive experiments on six benchmark datasets including two for massive multilingual NER and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method. With only 10 labeled examples for each class in each task, the proposed method achieves 10% improvement over state-of-the-art methods demonstrating its effectiveness for limited training labels regime.
Robust single-qubit gates by composite pulses in three-level systems

Composite pulses are an efficient tool for robust quantum control. In this work, we derive the form of the composite pulse sequence to implement robust single-qubit gates in a three-level system, where two low-energy levels act as a qubit. The composite pulses can efficiently cancel the systematic errors up to a certain order. We find that the three-pulse sequence cannot completely eliminate the first order of systematic errors, but still availably makes the fidelity resistant to variations in a specific direction. When employing more pulses in the sequence ($N>3$), the fidelity can be insensitive to the variations in all directions and the robustness region becomes much wider. Finally we demonstrate the applications of composite pulses in quantum information processing, e.g., robust quantum information transfer between two qubits.
Neuromorphic photonics: 2D or not 2D?

The computing industry is rapidly moving from a programming to a learning area, with the reign of the von Neumann architecture starting to fade, after many years of dominance. The new computing paradigms of non-von Neumann architectures have started leading to the development of emerging artificial neural network (ANN)-based analog electronic artificial intelligence (AI) chipsets with remarkable energy efficiency. However, the size and energy advantages of electronic processing elements are naturally counteracted by the speed and power limits of the electronic interconnects inside the circuits due to resistor-capacitor (RC) parasitic effects. Neuromorphic photonics has come forward as a new research field, which aims to transfer the well-known high-bandwidth and low-energy interconnect credentials of photonic circuitry in the area of neuromorphic platforms. The high potential of neuromorphic photonics and their well-established promise for fJ/Multiply-ACcumulate energy efficiencies at orders of magnitudes higher neuron densities require a number of breakthroughs along the entire technology stack, being confronted with a major advancement in the selection of the best-in-class photonic material platforms for weighting and activation functions and their transformation into co-integrated photonic computational engines. With this paper, we analyze the current status in neuromorphic computing and in available photonic integrated technologies and propose a novel three-dimensional computational unit which, with its compactness, ultrahigh efficiency, and lossless interconnectivity, is foreseen to allow scalable computation AI chipsets that outperform electronics in computational speed and energy efficiency to shape the future of neuromorphic computing.
How much intelligence is there in artificial intelligence? A 2020 update

Schank (1980) wrote an editorial for Intelligence on “How much intelligence is there in artificial intelligence?”. In this paper, we revisit this question. We start with a short overview of modern AI and showcase some of the AI breakthroughs in the four decades since Schank’s paper. We follow with a description of the main techniques these AI breakthroughs were based upon, such as deep learning and reinforcement learning; two techniques that have deep roots in psychology. Next, we discuss how psychologically plausible AI is and could become given the modern breakthroughs in AI’s ability to learn. We then access the main question of how intelligent AI systems actually are. For example, are there AI systems that can solve human intelligence tests? We conclude that Shank's observation, that intelligence is all about generalization and that AI is not particularly good at this, has, so far, withstood the test of time. Finally, we consider what AI insights could mean for the study of individual differences in intelligence. We close with how AI can further Intelligence research and vice versa, and look forward to fruitful interactions in the future.
Detecting sheep in UAV images

In the last decade, researchers have focused more on deep convolutional neural networks (CNNs) than other machine learning algorithms for object detection, localization, classification and segmentation. Such CNNs have achieved remarkable results in these fields and use the bounding boxes as the ground truth data. In this research article, we have used a fully connected network (FCN) for livestock detection in aerial images captured by an unmanned aerial vehicle (UAV), that used centroids as ground truth data. For performance evaluation and comparison, we have proposed a single-layered and a seven-layered CNN network in this article. These proposed networks are trained using state-of-the-art method, Region-based CNN. In addition, AlexNet, GoogLeNet, VGG16, VGG19 and ResNet50 were also fine-tuned for livestock detection. The results of the FCN and one of our proposed networks are then merged to improve the recall of the complete system from 90% to 98%.
Quantum Permutation Synchronization

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the binary nature of the problem, ensuring that the result is a set of permutations requires extra care. Hence, we: (i) show how to insert permutation constraints into a QUBO problem and (ii) solve the constrained QUBO problem on the current generation of the adiabatic quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global optimality with high probability while sampling the energy landscape to yield confidence estimates. Our proof-of-concepts realization on the adiabatic D-Wave computer demonstrates that quantum machines offer a promising way to solve the prevalent yet difficult synchronization problems.
Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation

Credit risk evaluation is a crucial yet challenging problem in financial analysis. It can not only help institutions reduce risk and ensure profitability, but also improve consumers' fair practices. The data-driven algorithms such as artificial intelligence techniques regard the evaluation as a classification problem and aim to classify transactions as default or non-default. Since non-default samples greatly outnumber default samples, it is a typical imbalanced learning problem and each class or each sample needs special treatment. Numerous data-level, algorithm-level and hybrid methods are presented, and cost-sensitive support vector machines (CSSVMs) are representative algorithm-level methods. Based on the minimization of symmetric and unbounded loss functions, CSSVMs impose higher penalties on the misclassification costs of minority instances using domain specific parameters. However, such loss functions as error measurement cannot have an obvious cost-sensitive generalization. In this paper, we propose a robust cost-sensitive kernel method with Blinex loss (CSKB), which can be applied in credit risk evaluation. By inheriting the elegant merits of Blinex loss function, i.e., asymmetry and boundedness, CSKB not only flexibly controls distinct costs for both classes, but also enjoys noise robustness. As a data-driven decision-making paradigm of credit risk evaluation, CSKB can achieve the "win-win" situation for both the financial institutions and consumers. We solve linear and nonlinear CSKB by Nesterov accelerated gradient algorithm and Pegasos algorithm respectively. Moreover, the generalization capability of CSKB is theoretically analyzed. Comprehensive experiments on synthetic, UCI and credit risk evaluation datasets demonstrate that CSKB compares more favorably than other benchmark methods in terms of various measures.
Image Splicing Detection using Deep Residual Network

Forgery using images are common nowadays. This may result in misleading the court, changing the mindset of people and defaming an individual. It is the need of the hour to design a tool that can detect forged and authenticated images. Image forgery detection schemes may be active or passive. Tampering detection schemes come into the category of passive or blind image forgery detection schemes. Deep Learning is a technique used to recognize or classify images into multiple class. Images are used as input for the convolutional neural network and processed through various layers for feature extraction and these extracted features are used as a training vector for the classifier model. This paper uses a pre-trained Deep Learning model resnet-50 for feature extraction from CASIA 2.0 dataset and three different classifiers for classification purpose.
A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps

Music streaming services heavily rely on recommender systems to improve their users' experience, by helping them navigate through a large musical catalog and discover new songs, albums or artists. However, recommending relevant and personalized content to new users, with few to no interactions with the catalog, is challenging. This is commonly referred to as the user cold start problem. In this applied paper, we present the system recently deployed on the music streaming service Deezer to address this problem. The solution leverages a semi-personalized recommendation strategy, based on a deep neural network architecture and on a clustering of users from heterogeneous sources of information. We extensively show the practical impact of this system and its effectiveness at predicting the future musical preferences of cold start users on Deezer, through both offline and online large-scale experiments. Besides, we publicly release our code as well as anonymized usage data from our experiments. We hope that this release of industrial resources will benefit future research on user cold start recommendation.
Transferable face image privacy protection based on federated learning and ensemble models

Abstract Face image features represent significant user privacy concerns. Face images cannot be privately transferred under existing privacy protection methods, and data across various social networks are unevenly distributed. This paper proposes a method for face image privacy protection based on federated learning and ensemble models. A federated learning model based on distributed data sets was established by means of federated learning. On the client side, a local facial recognition model was obtained by local face data training and used as the input of PcadvGAN to train PcadvGAN for several rounds. On the server side, a parameter aggregator based on a differential evolutionary algorithm was established as the discriminator of PcadvGAN server, and a client facial recognition model was ensembled simultaneously. The discriminator of the PcadvGAN server experienced mutation, crossover, and interaction with the ensemble model to reveal the optimal global weight of the PcadvGAN model. Finally, the global optimal aggregation parameter matrix of PcadvGAN was obtained by calculation. The server and the client shared the global optimal aggregation parameter matrix, enabling each client to generate private face images with high transferability and practicality. Targeted attack and non-targeted attack experiments demonstrated that the proposed method can generate high-quality, transferable, robust, private face images with only minor perturbations more effectively than other existing methods.