Reviewerly/exHarmony huggingface dataset:
The Evolution of DNS-based Email Authentication: Measuring Adoption and Finding Flaws

Email is still one of the most common ways of communication in our digital world, the underlying Simple Mail Transport Protocol (SMTP) is crucial for our information society. Back when SMTP was developed, security goals for the exchanged messages did not play a major role in the protocol design, resulting in many types of design limitations and vulnerabilities. Especially spear-phishing campaigns take advantage of the fact that it is easy to spoof the originating email address to appear more trustworthy. Furthermore, trusted brands can be abused in email spam or phishing campaigns. Thus, if no additional authentication mechanisms protect a given domain, attackers can misuse the domain. To enable proper authentication, various extensions for SMTP were developed in the past years.
Role of Gravitational Processes in the Migration of Heavy Metals in Soils of the Priolkhonye Mountain-Steppe Landscapes, Lake Baikal: Methodology of Research

The study object of this work is the soils of the Priolkhonye mountain-steppe landscapes. This research is performed at the Priolkhonye test site. Annual monitoring for exogenous geological process activity is conducted there. This research aims to study the influence of gravitational geological processes (landslides) on the migration of heavy metals, which are introduced into soil as a result of human agricultural activity. A methodology for the geoecological monitoring of soils based on an assessment of their contamination level by anthropogenic heavy metals, and a transfer mechanism throughout whole migration path (field–landslide–coast) is proposed. The following steps were taken to solve this problem: isolating a fine-grained fraction (&lt;100 μm); determining the Hg, Cd, Pb, and Cu concentrations using wavelength dispersive X-ray fluorescence and atomic absorption spectroscopies, as well as total organic carbon (TOC) content using UV-Vis spectrophotometry; computing various geochemical indices; and describing the migration path of these heavy metals and TOC. The monitoring results showed that these pollutants migrate to the coastal zone as a result of landslides activation. The soil contamination level is increased which can cause negative toxic effects in coastal ecosystems, and could rise to a dangerous level for humans. Thus, the observed changes in the indices and TOC values allow us to assess the geoecological state of the study area; namely, the periods of landslide activity and the supply of these pollutants to the coastal zone.
DSPI Filtering Evaluation Method Based on Sobel Operator and Image Entropy

Evaluating the filtering process of the digital speckle pattern interferometry (DSPI) of a single image by the traditional methods can result in under- or over-filtering. To solve this problem, this paper proposes an image-filtering evaluation method based on the Sobel operator and image entropy. First, the Sobel operator is introduced to convolute a filtered image to obtain a gradient image of a DSPI. Then, by analyzing the gradient image, image entropy is calculated according to the characteristics of the gradient map. Finally, the proposed method is verified by experiments. The experimental results show that the proposed method can quantitatively evaluate the filtering process of the DSPI.
Explaining deep learning of galaxy morphology with saliency mapping

ABSTRACT We successfully demonstrate the use of explainable artificial intelligence (XAI) techniques on astronomical data sets in the context of measuring galactic bar lengths. The method consists of training convolutional neural networks on human classified data from Galaxy Zoo in order to predict general galaxy morphologies, and then using SmoothGrad (a saliency mapping technique) to extract the bar for measurement by a bespoke algorithm. We contrast this to another method of using a convolutional neural network to directly predict galaxy bar lengths. These methods achieved correlation coefficients of 0.76 and 0.59, and root mean squared errors of 1.69 and 2.10 respective to human measurements. We conclude that XAI methods outperform conventional deep learning in this case, which could be reasonably explained by the larger data sets available when training the models. We suggest that our XAI method can be used to extract other galactic features (such as the bulge-to-disc ratio) without needing to collect new data sets or train new models. We also suggest that these techniques can be used to refine deep learning models as well as identify and eliminate bias within training data sets.
Bringing Friends into the Loop of Recommender Systems: An Exploratory Study

The recommender system (RS), as a computer-supported information filtering system, is ubiquitous and influences what we eat, watch, or even like. In online RS, interactions between users and the system form a feedback loop: users take actions based on the recommendations provided by RS, and RS updates its recommendations accordingly. As such interactions increase, the issue of recommendation homogeneity intensifies, which significantly impairs user experience. In the face of this long-standing issue, the newly-emerging social e-commerce offers a new solution -- bringing friends' recommendations into the loop (friend-in-the-loop). In this paper, we conduct an exploratory study on the benefits of friend-in-the-loop through mixed methods on a leading social e-commerce platform in China, Beidian. We reveal that friend-in-the-loop provides users with more accurate and diverse recommendations than merely RS, and significantly alleviates algorithmic homogeneity. Moreover, our qualitative results demonstrate that the introduction of friends' external knowledge, consumers' trust, and empathy accounts for these benefits. Overall, we elaborate that friend-in-the-loop comprehensively benefits both users and RS, and it is a promising HCI-based solution to recommendation homogeneity, which offers insightful implications on designing future human-algorithm collaboration models.
Learning policy scheduling for text augmentation

When training deep learning models, data augmentation is an important technique to improve the performance and alleviate overfitting. In natural language processing (NLP), existing augmentation methods often use fixed strategies. However, it might be preferred to use different augmentation policies in different stage of training, and different datasets may require different augmentation policies. In this paper, we take dynamic policy scheduling into consideration. We design a search space over augmentation policies by integrating several common augmentation operations. Then, we adopt a population based training method to search the best augmentation schedule. We conduct extensive experiments on five text classification and two machine translation tasks. The results show that the optimized dynamic augmentation schedules achieve significant improvements against previous methods.
Towards Lightweight Applications: Asymmetric Enroll-Verify Structure for Speaker Verification

With the development of deep learning, automatic speaker verification has made considerable progress over the past few years. However, to design a lightweight and robust system with limited computational resources is still a challenging problem. Traditionally, a speaker verification system is symmetrical, indicating that the same embedding extraction model is applied for both enrollment and verification in inference. In this paper, we come up with an innovative asymmetric structure, which takes the large-scale ECAPA-TDNN model for enrollment and the small-scale ECAPA-TDNNLite model for verification. As a symmetrical system, our proposed ECAPA-TDNNLite model achieves an EER of 3.07% on the Voxceleb1 original test set with only 11.6M FLOPS. Moreover, the asymmetric structure further reduces the EER to 2.31%, without increasing any computational costs during verification.
Distributed Multi-Target Tracking in Camera Networks

Most recent works on multi-target tracking with multiple cameras focus on centralized systems. In contrast, this paper presents a multi-target tracking approach implemented in a distributed camera network. The advantages of distributed systems lie in lighter communication management, greater robustness to failures and local decision making. On the other hand, data association and information fusion are more challenging than in a centralized setup, mostly due to the lack of global and complete information. The proposed algorithm boosts the benefits of the Distributed-Consensus Kalman Filter with the support of a re-identification network and a distributed tracker manager module to facilitate consistent information. These techniques complement each other and facilitate the cross-camera data association in a simple and effective manner. We evaluate the whole system with known public data sets under different conditions demonstrating the advantages of combining all the modules. In addition, we compare our algorithm to some existing centralized tracking methods, outperforming their behavior in terms of accuracy and bandwidth usage.
An integrated approach for determining objects to be relocated and their goal positions inside clutter for object retrieval

We consider the problem of rearranging objects in a cluttered and confined space using a robotic manipulator. The goal is to retrieve a target object from the clutter where the target is occluded by other objects. In situations where overhand grasps are not allowed, the robot needs to remove some objects to make the target accessible. In the course of removing the objects, the robot also needs to determine the locations to place the removed objects. If the robot can access enough empty spaces around or inside the clutter, the placement of the objects is trivially simple. If empty spaces are scarce, placing objects should be done in a principled way as an incorrect placement would deplete the empty spaces quickly.In this work, we propose a method that solves the problems of what and where to relocate objects inside the clutter to retrieve the target. Previously, there have been several efficient methods proposed that deal with each of the what and where to relocate problems separately. We solve the problems together using a graph structure constructed from an object configuration. Also, the method runs fast so scalable in the number of objects. Compared to a state-of-the-art method, our method reduces task and motion planning time up to 74.9% (at least 56.7%) and has a higher success rate under a short time limit for planning, which is 3 minutes.
Scale Expansion Pyramid Network for Cross-Scale Object Detection in Sar Images

In SAR images, there are objects with large scale difference, which is called cross-scale objects. For example, there are both large-scale airport objects and small-scale ship objects in SAR images. However, the current multi-scale object detection methods are difficult to detect objects with large scale difference. To address this issue, we propose a cross-scale object detection method for SAR images based on Scale Expansion Pyramid Network(SEPN) in this paper. The proposed SEPN can extract the salient features of the objects with a large scale difference, and by closely connecting the scale expansion layer with the convolutional layer during the downsampling process of the Feature Pyramid Network (FPN), the receptive field of the feature extracted by the convolutional layer can be adaptively extended, and finally it achieves the effect of cross-scale object detection in SAR image. Experiments on SSDD dataset and Gaofen-3 dataset show the effectiveness of our proposed methods in detecting objects of different scales in different scenes of SAR images.
CSVC-Net: Code-Switched Voice Command Classification using Deep CNN-LSTM Network

Colloquial Bengali has adopted many English words due to colonial influence. In conversational Bengali, it is quite common to speak in a mixture of English and Bengali, a phenomenon termed Code-switching (CS). To build a Voice Command Classifier in this era, when the usage of CS is ever-increasing, it is often necessary to map a single base command to its many different variants - spoken in multiple mixtures of languages. The works done with Bengali Speech have been primarily focused on single word classification and mostly incompetent in understanding the complex semantic relationships displayed in sentences. This paper proposes ‘CSVC-Net’, a CNN-LSTM based architecture for classifying spoken commands that exhibit code-switching between Bengali and English. To effectively reflect the scenario, it also presents a newly curated dataset named ‘Banglish’ containing 3,840 audio files of spoken computer commands belonging to 11 classes, considering 64 variations in total. The proposed pipeline passes the input audio signal through a series of appropriate transformation and augmentation steps enabling the model to achieve an accuracy of 92.08% on the curated dataset. Furthermore, the robustness of the proposed model has been justified by comparing with different architectures and tested under different noise levels with promising accuracy, which shows the applicability of the model in real-life scenarios.
Collaborative Fall Detection using a Wearable Device and a Companion Robot

Older adults who age in place face many health problems and need to be taken care of. Fall is a serious problem among elderly people. In this paper, we present the design and implementation of collaborative fall detection using a wearable device and a companion robot. First, we developed a wearable device by integrating a camera, an accelerometer and a microphone. Second, a companion robot communicates with the wearable device to conduct collaborative fall detection. The robot is also able to contact caregivers in case of emergency. The collaborative fall detection method consists of motion data based preliminary detection on the wearable device and video-based final detection on the companion robot. Both convolutional neural network (CNN) and long short-term memory (LSTM) are used for video-based fall detection. The experimental results show that the overall accuracy of video-based algorithm is 84%. We also investigated the relation between the accuracy and the number of image frames. Our method improves the accuracy of fall detection while maximizing the battery life of the wearable device. In addition, our method significantly increases the sensing range of the companion robot.
Generalized Few-Shot Video Classification With Video Retrieval and Feature Generation

Few-shot learning aims to recognize novel classes from a few examples. Although significant progress has been made in the image domain, few-shot video classification is relatively unexplored. We argue that previous methods underestimate the importance of video feature learning and propose to learn spatiotemporal features using a 3D CNN. Proposing a two-stage approach that learns video features on base classes followed by fine-tuning the classifiers on novel classes, we show that this simple baseline approach outperforms prior few-shot video classification methods by over 20 points on existing benchmarks. To circumvent the need of labeled examples, we present two novel approaches that yield further improvement. First, we leverage tag-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities. Second, we learn generative adversarial networks that generate video features of novel classes from their semantic embeddings. Moreover, we find existing benchmarks are limited because they only focus on 5 novel classes in each testing episode and introduce more realistic benchmarks by involving more novel classes, i.e., few-shot learning, as well as a mixture of novel and base classes, i.e., generalized few-shot learning. The experimental results show that our retrieval and feature generation approach significantly outperform the baseline approach on the new benchmarks.
Financial development, portfolio investments and the real economy in Africa

According to the conservative view, capital flows enhance economic growth. Focussing on Africa’s real economy, this study investigates the linkage between portfolio investments and real sector growth, and whether financial sector development strengthens this association. The study covers 30 countries over the period 1990–2017. We adopt the Lewbel instrumental variable general method of moments (IV-GMM) two-step robust estimator, which relies on heteroscedasticity for identification, while dealing with instrument insufficiency, unavailability, endogeneity and omitted variable bias. We found that portfolio equity has no growth impact on Africa’s real sector. Debt flows deter the growth of the overall real sector as well as the manufacturing and industrial sectors, but have no impact on agriculture and service growth. We found that financial development does strengthen the positive association between capital flows and economic growth, but this is dependent on the type of sector and portfolio investment, as well as on the degree of financial development. We control for known determinants of economic growth.
Robotic Indoor Scene Captioning from Streaming Video

Robots are usually equipped with cameras to explore the indoor scene and it is expected that the robot can well describe the scene with natural language. Although some great success has been achieved in image and video captioning technology, especially on many public datasets, the caption generated from indoor scene video is still not informative and coherent enough. In this paper, we propose the problem of Indoor Scene Captioning from Streaming Video, which aims at generating a more accurate and informative caption from streaming video. To solve this problem, we firstly design an algorithm to organize the visual information of the indoor scene into a scene graph, and then implement a scene graph guided captioning method, which takes the scene graph and video frames as input to generate the caption from the video streaming. The proposed framework is evaluated both on the AI2THOR dataset and a real-world robotic platform, demonstrating the effectiveness of the framework.
Interoperable Permissioned-Blockchain with Sustainable Performance

Bitcoin is an innovative and path-breaking technology that has influenced numerous industries across the globe. It is a form of digital currency (cryptocurrency) that can be used for trading and has the potential to replace fiat money, where the underlying infrastructure is called Blockchain. The Blockchain is an open ledger that provides decentralization, transparency, immutability, and confidentiality. Blockchain can be used in enormous applications, such as healthcare, logistics, supply chain management, the IoT, and so forth. Most of the industrial applications rely on the permissioned blockchain. However, the permissioned blockchain fails in some aspects, such as interoperability among different platforms. This paper suggests a sustainable system to solve the interoperability issue of the permissioned blockchain by designing a new infrastructure. This work has been tested in ethereum and hyperledger frameworks, which obtained a success rate of 100 percent.
Diverse Audio Captioning Via Adversarial Training

Audio captioning aims at generating natural language descriptions for audio clips automatically. Existing audio captioning models have shown promising improvement in recent years. However, these models are mostly trained via maximum likelihood estimation (MLE), which tends to make captions generic, simple and deterministic. As different people may describe an audio clip from different aspects using distinct words and grammars, we argue that an audio captioning system should have the ability to generate diverse captions for a fixed audio clip and across similar audio clips. To address this problem, we propose an adversarial training framework for audio captioning based on a conditional generative adversarial network (C-GAN), which aims at improving the naturalness and diversity of generated captions. Unlike processing data of continuous values in a classical GAN, a sentence is composed of discrete tokens and the discrete sampling process is non-differentiable. To address this issue, policy gradient, a reinforcement learning technique, is used to back-propagate the reward to the generator. The results show that our proposed model can generate more diverse captions, as compared to state-of-the-art methods.
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Joint registration of a stack of 2D histological sections to recover 3D structure (“3D histology reconstruction”) finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as “banana effect” (straightening of curved structures) and “z-shift” (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.