Reviewerly/exHarmony huggingface dataset:
Localized Trajectories for 2D and 3D Action Recognition

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.
Comparison of Tracking Techniques on 360-Degree Videos

With the availability of 360-degree cameras, 360-degree videos have become popular recently. To attach a virtual tag on a physical object in 360-degree videos for augmented reality applications, automatic object tracking is required so the virtual tag can follow its corresponding physical object in 360-degree videos. Relative to ordinary videos, 360-degree videos in an equirectangular format have special characteristics such as viewpoint change, occlusion, deformation, lighting change, scale change, and camera shakiness. Tracking algorithms designed for ordinary videos may not work well on 360-degree videos. Therefore, we thoroughly evaluate the performance of eight modern trackers in terms of accuracy and speed on 360-degree videos. The pros and cons of these trackers on 360-degree videos are discussed. Possible improvements to adapt these trackers to 360-degree videos are also suggested. Finally, we provide a dataset containing nine 360-degree videos with ground truth of target positions as a benchmark for future research.
Nearly-Linear uncertainty measures

Several easy to understand and computationally tractable imprecise probability models, like the Pari-Mutuel model, are derived from a given probability measure P0. In this paper we investigate a family of such models, called Nearly-Linear (NL). They generalise a number of well-known models, while preserving a simple mathematical structure. In fact, they are linear affine transformations of P0 as long as the transformation returns a value in [0,1]. We study the properties of NL measures that are (at least) capacities, and show that they can be partitioned into three major subfamilies. We investigate their consistency, which ranges from 2-coherence, the minimal condition satisfied by all, to coherence, and the kind of beliefs they can represent. There is a variety of different situations that NL models can incorporate, from generalisations of the Pari-Mutuel model, the ε-contamination model and other models to conflicting attitudes of an agent towards low/high P0-probability events (both prudential and imprudent at the same time), or to symmetry judgments. The consistency properties vary with the beliefs represented, but not strictly: some conflicting and partly irrational moods may be compatible with coherence. In a final part, we compare NL models with their closest, but only partly overlapping, models, neo-additive capacities and probability intervals.
Blockchain Standards and GovernmentApplications

Many people talk about blockchain but very few understand its true nature and potential. Blockchain seems very exciting yet simultaneously a bit confusing, and naturally many people, businesses, and governments approach it with high expectations while also exhibiting some hesitancy. This article will deal with the use of blockchain in relation to government applications. A proper assessment of such use requires a discussion on blockchain standards, which are currently developed, or may develop in the future. Without blockchain standards, any potential use of blockchain in government will be of limited and restricted value. This would render our discussion on government applications rather limited too. Standards enable us to appreciate blockchain applications in a useful way for future applications outside the context of government. Focusing our attention to government applications is deliberate. Blockchain, obviously, provides amazing opportunities for the private sector. Over the last years there has been widespread public disbelief in many public and government institutions. Corruption, fraud, lack of transparency, alienation and disconnection of citizen from decision-making centres oblige governments to change and offer proper governance conditions for their citizens. Further, higher consumer expectations in all sectors of the economy naturally affect the expectations of citizens vis-is their governments. For the above reasons, governments could leverage the positive features of blockchain to restore their reputation and efficiency towards citizens, by using blockchain. Of course, governments can use blockchain for many of their applications. To decide the proper application of blockchain within their operations, governments must embark on a need-based approach. Standards are extremely useful in such a need-based approach.
A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps

This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.
<i>shu-torjoma</i>: An English↔Bangla Statistical Machine Translation System

An efficient and publicly open machine translation system is in dire need to get the maximum benefits of Information and Communication Technology through removing the language barrier in this era of globalization.In this study, we present a Phrase-Based Statistical Machine Translation (PBMT) system between English and Bangla languages in both directions.To the best of our knowledge, the system is trained on the largest dataset of more than three million tokens each side in English↔Bangla translation task.In the system, we perform data preprocessing and use optimized parameters to produce efficient system output.We analyze our system output from several viewpoints: overall results, comparisons with the available systems, sentence type and length effect, and behaviour of two challenging linguistic propertiesprepositional phrase and noun inflection.Our analysis provides useful insights that translating into morphologically richer language is harder than translating from them and this is mainly due to the difficulties of translating noun inflections.Comparisons with the available systems show that our system outperforms the other systems significantly and gain 10.84 BLEU, 2.18 NIST and 19.02 TER points over the next best system.The analysis of the sentence type and length effect shows that simple sentences are easier to translate and the sentences longer than 15 words are harder to translate for English↔Bangla translation task.To foster the English↔Bangla machine translation research, we have developed development and test datasets, which are representative in sentence length and balanced in genre to be used as a benchmark and are made publicly available.
SEAL: Semisupervised Adversarial Active Learning on Attributed Graphs

Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional nonrelational data, how to make it effective over attributed graphs remains an open research question. Existing AL algorithms on node classification attempt to reuse the classic AL query strategies designed for nonrelational data. However, they suffer from two major limitations. First, different AL query strategies calculated in distinct scoring spaces are often naively combined to determine which nodes to be labeled. Second, the AL query engine and the learning of the classifier are treated as two separating processes, resulting in unsatisfactory performance. In this article, we propose a SEmisupervised Adversarial active Learning (SEAL) framework on attributed graphs, which fully leverages the representation power of deep neural networks and devises a novel AL query strategy for node classification in an adversarial way. Our framework learns two adversarial components; a graph embedding network that encodes both the unlabeled and labeled nodes into a common latent space, expecting to trick the discriminator to regard all nodes as already labeled, and a semisupervised discriminator network that distinguishes the unlabeled from the existing labeled nodes. The divergence score, generated by the discriminator in a unified latent space, serves as the informativeness measure to actively select the most informative node to be labeled by an oracle. The two adversarial components form a closed loop to mutually and simultaneously reinforce each other toward enhancing the AL performance. Extensive experiments on real-world networks validate the effectiveness of the SEAL framework with superior performance improvements to state-of-the-art baselines on node classification tasks.
VisTA: Integrating Machine Intelligence with Visualization to Support the Investigation of Think-Aloud Sessions

Think-aloud protocols are widely used by user experience (UX) practitioners in usability testing to uncover issues in user interface design. It is often arduous to analyze large amounts of recorded think-aloud sessions and few UX practitioners have an opportunity to get a second perspective during their analysis due to time and resource constraints. Inspired by the recent research that shows subtle verbalization and speech patterns tend to occur when users encounter usability problems, we take the first step to design and evaluate an intelligent visual analytics tool that leverages such patterns to identify usability problem encounters and present them to UX practitioners to assist their analysis. We first conducted and recorded think-aloud sessions, and then extracted textual and acoustic features from the recordings and trained machine learning (ML) models to detect problem encounters. Next, we iteratively designed and developed a visual analytics tool, VisTA, which enables dynamic investigation of think-aloud sessions with a timeline visualization of ML predictions and input features. We conducted a between-subjects laboratory study to compare three conditions, i.e., VisTA, VisTASimple (no visualization of the ML's input features), and Baseline (no ML information at all), with 30 UX professionals. The findings show that UX professionals identified more problem encounters when using VisTA than Baseline by leveraging the problem visualization as an overview, anticipations, and anchors as well as the feature visualization as a means to understand what ML considers and omits. Our findings also provide insights into how they treated ML, dealt with (dis)agreement with ML, and reviewed the videos (i.e., play, pause, and rewind).
Temporal Spiking Recurrent Neural Network for Action Recognition

In this paper, we propose a novel temporal spiking recurrent neural network (TSRNN) to perform robust action recognition in videos. The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidence reliable frames as spiking signals. The conventional CNN-RNNs typically used for this problem treat all the frames equally important such that they are error-prone to noisy frames. The TSRNN solves this problem by employing a temporal pooling architecture which can help RNN select sparse and reliable frames and enhances its capability in modelling long-range temporal information. Besides, a message passing bridge is added between the spiking signals and the recurrent unit. In this way, the spiking signals can guide RNN to correct its long-term memory across multiple frames from contamination caused by noisy frames with distracting factors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , occlusion, rapid scene transition). With these two novel components, TSRNN achieves competitive performance compared with the state-of-the-art CNN-RNN architectures on two large scale public benchmarks, UCF101 and HMDB51.
I Know What You Enter on Gear VR

Virtual reality (VR) techniques offer users immersive experiences in a virtual environment (VE) where they can enjoy gaming, training, shopping and social activities. However, security and privacy issues of VR are rarely investigated. In this paper, we introduce novel computer vision-based and motion sensor-based side channel attacks to infer keystrokes in a virtual environment. In the computer vision-based attack, a stereo camera records a user maneuvering a pointing device and inputting passwords. Various computer vision techniques are used to detect the touching frames in which the user taps a touchpad to enter keys on the virtual keyboard within the VE. We then estimate the pose of the headset and derive the orientation of the virtual pointing ray in each touching frame. In the motion sensor-based attack, we exploit sensors including accelerometer, gyroscope and magnetometer built inside the pointing device to retrieve orientation angles of the pointing device. With orientation angles of the pointing device, we can derive the empirical rotation angles and infer the clicked keys by assessing the similarity between the empirical rotation angles and the reference ones between keys. Extensive real-world experiments are performed to demonstrate the feasibility and effectiveness of these side channel attacks.
Mobile Language Learning Innovation Inspired by Migrants

Migrants arriving in a country are not always welcome. Similarly, the arrival of new technologies can be perceived as a blot on the familiar landscape of established educational practices. This paper seeks a productive synergy between migrants’ educational requirements with respect to learning the language of their host society; their valuable and unique human experiences and talents; and innovative learning designs that harness the ubiquity of smartphones and other mobile technologies. The present-day mass mobility and migration of individuals and groups of people sows the seeds of new ideas, generating novel approaches to language teaching and learning supported by personal technologies. There is a substantial body of evidence from research and practice for the effectiveness and appeal of mobile language learning in various educational settings, however, a specific focus on migrant learners is a more recent development. The paper provides an analysis of innovative mobile language learning projects and applications designed for migrants. It is argued that innovations resulting from a concern with supporting migrant learners can also benefit other mobile populations, such as students and business people, through the introduction of more adaptable ways of fostering and organizing learning.
Quantum cryptography for secured communication networks

Quantum cryptography is a method for accessing data with the cryptosystem more efficiently. The network security and the cryptography are the two major properties in securing the data in the communication network. The quantum cryptography uses the single photon passing through the polarization of a photon. In Quantum Cryptography, it's impossible for the eavesdropper to copy or modify the encrypted messages in the quantum states in which we are sending through the optical fiber channels. Cryptography performed by using the protocols BB84 and B92 protocols. The two basic algorithms of quantum cryptography are Shor’s algorithm and the Grover’s’s algorithm. For finding the number of integer factorization of each photon, Shor’s algorithm is used. Grover’s’s algorithm used for searching the unsorted data. Shor’s algorithm overcomes RSA algorithm by high security. By the implementation of quantum cryptography, we are securing the information from the eavesdropper and thereby preventing data in the communication channel.
A Domain Agnostic Measure for Monitoring and Evaluating GANs

Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training. The latter cannot be determined by simply inspecting the generator and discriminator loss curves as they behave non-intuitively. We leverage the notion of duality gap from game theory to propose a measure that addresses both (i) and (ii) at a low computational cost. Extensive experiments show the effectiveness of this measure to rank different GAN models and capture the typical GAN failure scenarios, including mode collapse and non-convergent behaviours. This evaluation metric also provides meaningful monitoring on the progression of the loss during training. It highly correlates with FID on natural image datasets, and with domain specific scores for text, sound and cosmology data where FID is not directly suitable. In particular, our proposed metric requires no labels or a pretrained classifier, making it domain agnostic.
Multi-View Geometric Mean Metric Learning for Kinship Verification

This paper proposes a multi-view geometric mean metric learning (MvGMML) method for the real-world kinship verification from facial images. Unlike existing kinship verification methods which dramatically degrade their performance when facial images are not well aligned, we present an efficient misalignment-robust kinship verification framework. First, a facial feature detector is employed to localize several facial feature points such as the right and left corners of two eyes. Then, a dense SIFT descriptor is extracted around each feature point. Lastly, our proposed MvGMML method jointly learns multiple local geometric mean metrics, one geometric mean metric for each view (i.e., feature point), to better exploit complementary information of all views. Experimental results on two widely used kinship datasets are presented to show the efficacy of our method.
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature

We introduce a new task of modeling the role and function for on-line resource citations in scientific literature.By categorizing the online resources and analyzing the purpose of resource citations in scientific texts, it can greatly help resource search and recommendation systems to better understand and manage the scientific resources.For this novel task, we are the first to create an annotation scheme, which models the different granularity of information from a hierarchical perspective.And we construct a dataset SciRes, which includes 3,088 manually annotated resource contexts.In this paper, we propose a possible solution by using a multi-task framework to build the scientific resource classifier (SciResCLF) for jointly recognizing the role and function types.Then we use the classification results to help a scientific resource recommendation (SciResREC) task.Experiments show that our model achieves the best results on both the classification task and the recommendation task.The SciRes dataset 1 will be released for future research.
Towards Zero-shot Language Modeling

Can we construct a neural model that is inductively biased towards learning human languages?Motivated by this question, we aim at constructing an informative prior over neural weights, in order to adapt quickly to heldout languages in the task of character-level language modeling.We infer this distribution from a sample of typologically diverse training languages via Laplace approximation.The use of such a prior outperforms baseline models with an uninformative prior (so-called 'finetuning') in both zero-shot and few-shot settings.This shows that the prior is imbued with universal phonological knowledge.Moreover, we harness additional language-specific side information as distant supervision for held-out languages.Specifically, we condition language models on features from typological databases, by concatenating them to hidden states or generating weights with hypernetworks.These features appear beneficial in the few-shot setting, but not in the zero-shot setting.Since the paucity of digital texts affects the majority of the world's languages, we hope that these findings will help broaden the scope of applications for language technology.
Interpretable Relevant Emotion Ranking with Event-Driven Attention

Multiple emotions with different intensities are often evoked by events described in documents.Oftentimes, such event information is hidden and needs to be discovered from texts.Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results.However, existing studies often ignore the latent event information.In this paper, we proposed a novel interpretable relevant emotion ranking model with the event information incorporated into a deep learning architecture using the event-driven attentions.Moreover, corpuslevel event embeddings and document-level event distributions are introduced respectively to consider the global events in corpus and the document-specific events simultaneously.Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label approaches.Moreover, interpretable results can be obtained to shed light on the events which trigger certain emotions.
A Preliminary Study of English Mobile Learning Model Based on Constructivism

This paper aims at exploring the model of English mobile learning system based on constructivism. Firstly, it defines the concepts of constructivism, constructivism learning theory, mobile learning and mobile learning theory. Then, it summarizes and analyses the relevant researches on mobile learning at home and abroad. Then it elaborates the purpose, significance and research value of the topic, respectively. The research hypothesis is put forward, and the English mobile learning model based on constructivist learning theory is preliminarily expounded. Finally, the research ideas, research methods, technical routes and implementation steps are explained.