Reviewerly/exHarmony huggingface dataset:
Monetary Policy and Financial Inclusion in Sub-Sahara Africa: A Panel VAR Approach

This article investigates the dynamic and bi-causal link between monetary policy and financial inclusion in sub-Saharan Africa using a panel VAR framework. The researcher obtained data from World Development Indicators (WDI) spanning from 1990 to 2014 for 48 sub-Saharan African economies. The findings suggest that a bi-causal relationship exists between monetary policy and financial inclusion. Specifically, it is evident that monetary policy affects financial inclusion, and financial inclusion is also influenced by monetary policy. The policy implication of this study is that the effectiveness of monetary policy depends on financial inclusion. Hence, the efforts of governments in sub-Saharan African countries should aim at policies that enhance financial inclusion for effective implementation of monetary policy. Also, promoting financial inclusion will require governments in sub-Saharan Africa to reduce their monetary policy rates.
Leakage Resilient Secret Sharing and Applications

A secret sharing scheme allows a dealer to share a secret among a set of n parties such that any authorized subset of the parties can recover the secret, while any unauthorized subset learns no information about the secret. A leakage-resilient secret sharing scheme (introduced in independent works by Goyal and Kumar, STOC '18 and Benhamouda, Degwekar, Ishai and Rabin, CRYPTO '18) additionally requires the secrecy to hold against every unauthorized set of parties even if they obtain some bounded leakage from every other share. The leakage is said to be local if it is computed independently for each share. So far, the only known constructions of local leakage resilient secret sharing schemes are for threshold access structures for very low (O(1)) or very high ( $$n -o(\log n)$$ ) thresholds. In this work, we give a compiler that takes a secret sharing scheme for any monotone access structure and produces a local leakage resilient secret sharing scheme for the same access structure, with only a constant-factor asymptotic blow-up in the sizes of the shares. Furthermore, the resultant secret sharing scheme has optimal leakage-resilience rate, i.e., the ratio between the leakage tolerated and the size of each share can be made arbitrarily close to 1. Using this secret sharing scheme as the main building block, we obtain the following results:
Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns of different scales. The model can be learned from the training video sequences by an “analysis by synthesis” learning algorithm that iterates the following two steps. Step 1 synthesizes video sequences from the currently learned model. Step 2 then updates the model parameters based on the difference between the synthesized video sequences and the observed training sequences. We show that the learning algorithm can synthesize realistic dynamic patterns. We also show that it is possible to learn the model from incomplete training sequences with either occluded pixels or missing frames, so that model learning and pattern completion can be accomplished simultaneously.
High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks

High-risk prediction of cardiovascular disease is of great significance and impendency in medical fields with the increasing phenomenon of sub-health these years. Most existing pathological methods for the prognosis prediction are either costly or prone to misjudgement. Therefore, plenty of automated models based on machine learning have been proposed to predict the onset of cardiovascular disease with the premorbid information of patients extracted from their historical Electronic Health Records (EHRs). However, it is a tough job to select proper features from longitudinal and heterogeneous EHRs, and also a great challenge to obtain accurate and robust representations for patients. In this paper, we propose an entirely end-to-end model called DeepRisk based on attention mechanism and deep neural networks, which can not only learn high-quality features automatically from EHRs, but also efficiently integrate heterogeneous and time-ordered medical data, and finally predict patients' risk of cardiovascular diseases. Experiments are carried out on a real medical dataset and results show that DeepRisk can significantly improve the high-risk prediction accuracy for cardiovascular disease compared with state-of-the-art approaches.
Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

Ship category classification in high-resolution aerial images has attracted great interest in applications such as maritime security, naval construction, and port management. However, the applications of previous methods were mainly limited by the following issues: (i) The existing ship category classification methods were mainly to classify on accurately-cropped image patches. This is unsatisfactory for the results of the existing methods in practical applications, because the location of the ship in the patch obtained by the object detection varies greatly. (ii) The factors such as target scale variations and class imbalance have a great influence on the performance of ship category classification. Aiming at the issues above, we propose a novel ship detection and category classification framework. The category classification is based on accurate location. The detection network can generate more precise rotated bounding boxes in large-scale aerial images by introducing a novel Sequence Local Context (SLC) module. Besides, three different ship category classification networks are proposed to eliminate the effect of scale variations, and the Spatial Transform Crop (STC) operation is used to get aligned image patches. Whatever the problem of insufficient samples or class imbalance have, the Proposals Simulation Generator (PSG) is considered to handle this properly. Most remarkably, the state-of-the-art performance of our framework is demonstrated by experiments based on the 19-class ship dataset HRSC2016 and our multiclass warship dataset.
Fully Secure Attribute-Based Encryption for t-CNF from LWE

Attribute-based Encryption (ABE), first introduced by [SW05, GPSW06], is a public key encryption system that can support multiple users with varying decryption permissions. One of the main properties of such schemes is the supported function class of policies. While there are fully secure constructions from bilinear maps for a fairly large class of policies, the situation with lattice-based constructions is less satisfactory and many efforts were made to close this gap. Prior to this work the only known fully secure lattice construction was for the class of point functions (also known as IBE). In this work we construct for the first time a lattice-based (ciphertext-policy) ABE scheme for the function class t-CNF, which consists of CNF formulas where each clause depends on at most t bits of the input, for any constant t. This class includes NP-verification policies, bit-fixing policies and t-threshold policies. Towards this goal we also construct a fully secure single-key constrained PRF from OWF for the same function class, which might be of independent interest.
Redirected Jumping: Imperceptibly Manipulating Jump Motions in Virtual Reality

Jumping is a fundamental movement in our daily lives that is often used in many video games. However, little research has been done on jumping and its possible use as a redirection technique in virtual reality (VR). In this study we explore Redirected Jumping, a novel redirection technique which enables us to purposefully manipulate the mapping of the user's physical jumping movements (e.g., distance and direction) to movement in the virtual space, allowing richer and more active physical VR experiences within a limited tracking area. To demonstrate the possibilities afforded by Redirected Jumping, we implemented a jump detection algorithm and jumping redirection methods for three basic jumping actions (i.e., horizontal, vertical, and rotational jumps) using common VR devices. We conducted three user studies to investigate the effective manipulation ranges, and the results revealed that our methods can manipulate a user's jumping movements without his/her noticing, similar to walking.
Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks

Rheumatoid arthritis is an autoimmune disease that causes chronic inflammation of synovial joints, often resulting in irreversible structural damage. The activity of the disease is evaluated by clinical examinations, laboratory tests, and patient self-assessment. The long-term course of the disease is assessed with radiographs of hands and feet. The evaluation of the X-ray images performed by trained medical staff requires several minutes per patient. We demonstrate that deep convolutional neural networks can be leveraged for a fully automated, fast, and reproducible scoring of X-ray images of patients with rheumatoid arthritis. A comparison of the predictions of different human experts and our deep learning system shows that there is no significant difference in the performance of human experts and our deep learning model.
On the role of data balancing for machine learning-based code smell detection

Code smells can compromise software quality in the long term by inducing technical debt. For this reason, many approaches aimed at identifying these design flaws have been proposed in the last decade. Most of them are based on heuristics in which a set of metrics (e.g., code metrics, process metrics) is used to detect smelly code components. However, these techniques suffer of subjective interpretation, low agreement between detectors, and threshold dependability. To overcome these limitations, previous work applied Machine Learning techniques that can learn from previous datasets without needing any threshold definition. However, more recent work has shown that Machine Learning is not always suitable for code smell detection due to the highly unbalanced nature of the problem. In this study we investigate several approaches able to mitigate data unbalancing issues to understand their impact on ML-based approaches for code smell detection. Our findings highlight a number of limitations and open issues with respect to the usage of data balancing in ML-based code smell detection.
Natural Language Guided Visual Relationship Detection

Reasoning about the relationships between object pairs in images is a crucial task for holistic scene understanding. Most of the existing works treat this task as a pure visual classification task: each type of relationship or phrase is classified as a relation category based on the extracted visual features. However, each kind of relationships has a wide variety of object combination and each pair of objects has diverse interactions. Obtaining sufficient training samples for all possible relationship categories is difficult and expensive. In this work, we propose a natural language guided framework to tackle this problem. We propose to use a generic bi-directional recurrent neural network to predict the semantic connection between the participating objects in the relationship from the aspect of natural language. The proposed simple method achieves the state-of-the-art on the Visual Relationship Detection (VRD) and Visual Genome datasets, especially when predicting unseen relationships (e.g., recall improved from 76.42% to 89.79% on VRD zeroshot testing set).
Self-Attention Relation Network for Few-Shot Learning

The success of deep learning greatly attributes to massive data with accurate labels. However, for few shot learning, especially zero shot learning, deep models cannot be well trained in that there are few available labeled samples. Inspired by human visual system, attention models have been widely used in action recognition, instance segmentation, and other vision tasks by introducing spatial, temporal, or channel-wise weights. In this paper, we propose a self-attention relation network (SARN) for few-shot learning. SARN consists of three modules, i.e., embedding module, attention module and relation module. The embedding module extracts feature maps while the attention module is introduced to enhance the learned features. Finally the extracted features of the query sample and support set are fed into the relation module for comparison, and the relation score is output for classification. Compared with the existing relation network for few shot learning, SARN can discover non-local information and allow long-range dependency. SARN can be easily extended to zero shot learning by replacing the support set with semantic vectors. Experiments on benchmarks (Omniglot, miniImageNet, AwA, and CUB) show that our proposed SARN outperforms the state-of-the-art algorithms in terms of both few shot and zero shot tasks.
Comparison of Photonic Reservoir Computing Systems for Fiber Transmission Equalization

In recent years, various methods, architectures, and implementations have been proposed to realize hardware-based reservoir computing (RC) for a range of classification and prediction tasks.Here we compare two photonic platforms that owe their computational nonlinearity to an optically injected semiconductor laser and to the optical transmission function of a Mach-Zehnder modulator, respectively.We numerically compare these platforms in a delay-based reservoir computing framework, in particular exploring their ability to perform equalization tasks on nonlinearly distorted signals at the output of a fiber-optic transmission line.Although the non-linear processing provided by the two systems is different, both produce a substantial reduction of the bit-error-rate (BER) for such signals of up to several orders of magnitude.We show that the obtained equalization performance depends significantly on the operating conditions of the physical systems, the size of the reservoir and the output layer training method.
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference

Automation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible since current black-box approaches lack explainable reasoning. We argue that both the visual interface and model structure of deep learning systems need to take into account interaction design. We propose a framework of collaborative semantic inference (CSI) for the co-design of interactions and models to enable visual collaboration between humans and algorithms. The approach exposes the intermediate reasoning process of models which allows semantic interactions with the visual metaphors of a problem, which means that a user can both understand and control parts of the model reasoning process. We demonstrate the feasibility of CSI with a co-designed case study of a document summarization system.
A Finger-Worn Device for Exploring Chinese Printed Text With Using CNN Algorithm on a Micro IoT Processor

This study designed a finger-worn device—named the Chinese FingerReader—that can be practically applied by visually impaired users for recognizing traditional Chinese characters on the micro internet of things (IoT) processor. The device is portable, easy to operate, and designed to be worn on the index finger. The Chinese FingerReader on the index finger contains a small camera and buttons. The small camera captures images by identifying the relative position of the index finger to the printed text, and the buttons are applied to capture an image by visually impaired users and provide the audio output of the corresponding Chinese character by a voice prompt. To recognize Chinese characters, English letters, and numbers, a robust Chinese optical character recognition (OCR) system was developed according to the training strategy of an augmented convolution neural network algorithm. The proposed Chinese OCR system can segment a single character from the captured image, and the system can accurately recognize rotated Chinese characters. The experimental results revealed that compared with the OCR application programming interfaces of Google and Microsoft, the proposed OCR system obtains 95% accuracy rate in dealing with rotated character images where the Google and Microsoft OCR APIs only obtain 65% and 34% accuracy rates. These results illustrate that the proposed OCR system was more suitable for the needs of visually impaired people in actual use. Finally, three usage scenarios were simulated, and the accuracy and operational performance of the system were tested. Field tests of this system were conducted for visually impaired users to verify its feasibility.
BRIDGE: Byzantine-Resilient Decentralized Gradient Descent

Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multi-agent and Internet-of-Things systems) or computational/privacy reasons (e.g., large-scale machine learning on smartphone data). Such applications often require the learning tasks to be carried out in a decentralized fashion, in which there is no central server that is directly connected to all nodes. In real-world decentralized settings, nodes are prone to undetected failures due to malfunctioning equipment, cyberattacks, etc., which are likely to crash non-robust learning algorithms. The focus of this paper is on robustification of decentralized learning in the presence of nodes that have undergone Byzantine failures. The Byzantine failure model allows faulty nodes to arbitrarily deviate from their intended behaviors, thereby ensuring designs of the most robust of algorithms. But the study of Byzantine resilience within decentralized learning, in contrast to distributed learning, is still in its infancy. In particular, existing Byzantine-resilient decentralized learning methods either do not scale well to large-scale machine learning models, or they lack statistical convergence guarantees that help characterize their generalization errors. In this paper, a scalable, Byzantine-resilient decentralized machine learning framework termed <b>B</b>yzantine-<b>r</b>es<b>i</b>lient <b>d</b>ecentralized <b>g</b>radient d<b>e</b>scent (BRIDGE) is introduced. Algorithmic and statistical convergence guarantees for one variant of BRIDGE are also provided in the paper for both strongly convex problems and a class of nonconvex problems. In addition, large-scale decentralized learning experiments are used to establish that the BRIDGE framework is scalable and it delivers competitive results for Byzantine-resilient convex and nonconvex learning.
I Can Hear Your Alexa: Voice Command Fingerprinting on Smart Home Speakers

Millions of smart home speakers, such as Amazon Echo and Google Home, have been purchased by U.S. consumers. However, the security and privacy of smart home speakers have not been rigorously examined, which raise critical security and privacy concerns. In this paper, we investigate untold and severe privacy leakage of smart home speakers. Specifically, we examine a new passive attack, referred to as voice command fingerprinting attack, on smart home speakers. We demonstrate that a passive attacker, who can only eavesdrop encrypted traffic between a smart home speaker and a cloud server, can infer users' voice commands and compromise the privacy of millions of U.S. consumers. We formulate the attacks by harnessing machine learning algorithms. In addition to leveraging accuracy, we propose a new privacy metric, named semantic distance, to assess the privacy leakage with natural language processing. Our experiment results on a real-world dataset suggest that voice command fingerprinting attacks can correctly infer 33.8% of voice commands by eavesdropping encrypted traffic. Our results also show that existing padding methods can diminish an attacker's accuracy to 14.7%, but would cause high communication overhead (548%) and long time delay (330%).
Three-Dimensional Route Planning Based on the Beetle Swarm Optimization Algorithm

Swarm intelligence algorithms have better intelligence and adaptation compared with the traditional route planning method. A three-dimensional route planning method based on the beetle swarm optimization (BSO) algorithm was proposed. The iterative updating strategy of the BSO algorithm cooperated with the search mechanism of the beetle monomer and the updating strategy of the particle swarm optimization (PSO) algorithm, thus accelerating iterative convergence and decreasing the probability of trapping in the local optimal solution of the algorithm. The practical engineering problem of three-dimensional route planning was addressed by processing uneven ground barriers using the penalty function, and a smooth route is gained from cubic spline interpolation. In this study, a three-dimensional environmental model was constructed by using actual elevation data from the USGS/NASA SRTM, and a simulation experiment of three-dimensional route planning was performed using the proposed method. The proposed method was compared with other algorithms. Experimental results demonstrated that when the iteration time was set to 50, the route planning length based on BSO algorithm was about 90% of the route planning based on the PSO algorithm. Moreover, the proposed route planning method had high convergence rate and stable convergence result and is applicable to three-dimensional route planning.
Multi-Task Learning with Compressible Features for Collaborative Intelligence

A promising way to deploy Artificial Intelligence (AI)-based services on mobile devices is to run a part of the AI model (a deep neural network) on the mobile itself, and the rest in the cloud. This is sometimes referred to as collaborative intelligence. In this framework, intermediate features from the deep network need to be transmitted to the cloud for further processing. We study the case where such features are used for multiple purposes in the cloud (multi-tasking) and where they need to be compressible in order to allow efficient transmission to the cloud. To this end, we introduce a new loss function that encourages feature compressibility while improving system performance on multiple tasks. Experimental results show that with the compression-friendly loss, one can achieve around 20% bitrate reduction without sacrificing the performance on several vision-related tasks.
Efficient Deep Learning Hyperparameter Tuning Using Cloud Infrastructure: Intelligent Distributed Hyperparameter Tuning with Bayesian Optimization in the Cloud

The paper discusses how we can leverage cloud infrastructure for efficient hyperparameter tuning of deep neural networks on high dimensional hyperparameter spaces using Bayesian Optimization. The paper experiments Bayesian optimization in the cloud at different levels of concurrency for the warmup runs for the Bayesian optimization. Two different distributed hyperparameter tuning approaches were experimented in the cloud - Training on multiple nodes with higher warm-up concurrency Vs Distributed Training on multiple nodes with Horovod and reduced number of warm-up runs. The results indicate that greater number of warm-up runs for Bayesian optimization results in better exploration of the search space. The hyper parameter tuning and distributed training with Horovod in the cloud was performed using the HyperDrive framework of Azure Machine Learning Service for Video Activity Recognition problem. The experiment used a Long-term Recurrent Convolutional Network (LRCN) with transfer learning from Resnet50 backbone.