Reviewerly/exHarmony huggingface dataset:
Zero-Shot Learning via Discriminative Dual Semantic Auto-Encoder

Zero-shot learning (ZSL) is an effective method to perform the recognition task without any training samples of specific classes.Most existing ZSL models put emphasis on learning an embedding between visual space and semantic space directly.However, few ZSL models research whether the humandesigned semantic features are discriminative enough to recognize different classes.Moreover, one-way mapping suffers from the project domain shift problem.In this article, we propose to learn a Discriminative Dual Semantic Auto-encoder (DDSA) based on the encoder-decoder paradigm to solve this problem.DDSA attempts to construct two bidirectional embeddings to connect the visual space and the semantic space with the help of the learned aligned space which includes discriminative information of the visual features and semantic features.Based on the DDSA, we additionally propose a Deep DDSA to capture deep aligned features that are more conducive to zero-shot classification.The key to the proposed framework is that it implicitly exact the principal information from visual space and semantic space to construct aligned features, which is not only semantic-preserving but also discriminative.Extensive experiments on five benchmarks (SUN, CUB, AWA1, AWA2 and aPY) demonstrate the effectiveness of the proposed framework with stateof-the-art performance obtained on both conventional ZSL and generalized ZSL settings.
Leveraging Pre-Trained Language Model for Summary Generation on Short Text

Bidirectional Encoder Representations from Transformers represents the latest incarnation of pre-trained language models which have been obtained a satisfactory effect in text summarization tasks. However, it has not achieved good results for the generation of Chinese short text summaries. In this work, we propose a novel short text summary generation model based on keyword templates, which uses templates found in training data to extract keywords to guide summary generation. The experimental results of the LCSTS data set show that our model performs better than the baseline model. The analysis shows that the methods used in our model can generate high-quality summaries.
Event-Driven Query Expansion

A significant number of event-related queries are issued in Web search. In this paper, we seek to improve retrieval performance by leveraging events and specifically target the classic task of query expansion. We propose a method to expand an event-related query by first detecting the events related to it. Then, we derive the candidates for expansion as terms semantically related to both the query and the events. To identify the candidates, we utilize a novel mechanism to simultaneously embed words and events in the same vector space. We show that our proposed method of leveraging events improves query expansion performance significantly compared with state-of-the-art methods on various newswire TREC datasets.
Detection of cyber harassment (cyberbullying) on Instagram using naïve bayes classifier with bag of words and lexicon based features

Instagram is a very popular social media across the world, with varied users from teenagers to adults. By using Instagram people are able to share photos or videos through social networks. Instagram also provides a lot of features, one of the features is the comment section. However, there are so many Instagram users who make social media as a platform to harass others. Bullying or harassing can affect the psychological condition and in the extreme condition can drive people to suicide. The focus of this research is to detect cyberbullying on Instagram comment into two classes, one is classified as cyberbullying and the other is non-cyberbullying. If we can successfully detect cyberbullying comment, it should help to prevent the cyberbullying act before it happens. The detection process consists of several steps, starts with preprocessing, followed by feature extraction, and the last is classification or in this case, cyberbullying detection. In this research, Naïve Bayes classifier with Bag of Words and Lexicon based features is employed to detect the cyberbullying. The Bag of Words features are extracted from the terms occurred in the comment and Lexicon-based features are extracted by using a dictionary or commonly known as sentiment lexicon. Since Indonesian is a low resource language, it is interesting and challenging to investigate this topic by using Indonesian dataset. In this experiment, the highest evaluation results are obtained by combining Bag of Word features and Lexicon-based features than using the features independently. We use 5-fold cross-validation and the system yields accuracy 0.872, precision 0.948, recall 0.824, and f-measure 0.874.
Revitalization of Indigenous Languages through Pre-processing and Neural Machine Translation: The case of Inuktitut

Indigenous languages have been very challenging when dealing with NLP tasks and applications because of multiple reasons.These languages, in linguistic typology, are polysynthetic and highly inflected with rich morphophonemics and variable dialectal-dependent spellings; which affected studies on any NLP task in the recent years.Moreover, Indigenous languages have been considered as low-resource and/or endangered; which poses a great challenge for research related to Artificial Intelligence and its fields, such as NLP and machine learning.In this paper, we propose a study on the Inuktitut language through pre-processing and neural machine translation, in order to revitalize the language which belongs to the Inuit family, a type of polysynthetic languages spoken in Northern Canada.Our focus is concentrated on: (1) the preprocessing phase, and (2) applications on specific NLP tasks such as morphological analysis and neural machine translation, both for Indigenous languages of Canada.Our evaluations in the context of lowresource Inuktitut-English Neural Machine Translation, showed significant improvements of the proposed approach compared to the state-of-the-art.
A Privacy Filter Framework for Internet of Robotic Things Applications

Traditionally robots have been stand-alone systems. In recent years, however, they have increasingly been connected to external knowledge resources through the Internet of Things (IoT). These robots are thus becoming part of IoT and can realistically allocate Internet of Robotic Things (IoRT) technologies. IoRT can facilitate Human-Robot Interaction (HRI) at functional (commanding and programming) and social levels, as well as a means for remote-interaction. IoRT-HRI can cause privacy issues for humans, in part because robots can collect data using IoT and move in the real world, partly because robots can learn to read human social cues and adapt or correct their behavior accordingly. In this paper, we address the topic of privacy-preserving for IoRT- Hri applications. The objective is to design a data release framework called a Privacy Filter (PF) that can prevent an adversary from private mining information from the released data while keeping utility data. In the experiments, we test our framework on two accessible datasets: MNIST (hand-written digits) and UCI-HAR (activity recognition from motion). Our experimental results on these datasets show that PF is highly effective in removing private information from the dataset while allowing utility data to be mined effectively.
Deep learning for detection of text polarity in natural scene images

Text extraction and recognition from natural scene images is a challenging task due to their complex background. It has several computer vision applications like license plate recognition, content based image retrieval, digitization for visually impaired etc. In these images, dark text can be present on a bright background or vice versa and there is an imperative need to determine this polarity for the recognition process. In the present work, we have proposed to use deep learning approaches to determine text polarity. We have used Convolutional Neural Network (CNN) to classify whether a scene image contains dark text on a bright background or vice versa. CNN has been trained on image samples collected from benchmarking datasets like ICDAR, IIIT5K etc. We have also extracted CNN features by removing its final fully connected layers and trained support vector machine (SVM) classifier using these features. Our experiments have shown that this transfer learning approach has given better accuracy than original CNN and the corresponding results are reported.
Formal Verification for Safe Deep Reinforcement Learning in Trajectory Generation

We consider the problem of Safe Deep Reinforcement Learning (DRL) using formal verification in a trajectory generation task. In more detail, we propose an approach to verify whether a trained model can generate trajectories that are guaranteed to meet safety properties (e.g., operate in a limited work-space). We show that our verification approach based on interval analysis, provably guarantees whether a model meets pre-specified safety properties and it returns the input values that cause a violation of such properties. Furthermore, we show that an optimized DRL approach (i.e., using scaling discount factor and a mixed exploration policy based on a directional controller) can reach the target with millimeter precision while reducing the set of inputs that cause safety violations. Crucially, in our experiments, the number of undesirable inputs is so low that they can be directly removed with a simple controller.
Presenting the Multi-view Traffic Intersection Dataset (MTID): A Detailed Traffic-Surveillance Dataset

This work presents a novel and unique traffic surveillance dataset, the MTID.When capturing data for traffic surveillance, the positioning of the camera is crucial, and depending on the task different approaches provide different advantages.Multiple viewpoints, however, are rarely compared.Our dataset gives the ability to analyze the difference between two viewpoints in great detail.A complex traffic scene has been captured simultaneously from two different viewpoints: that of a camera mounted on existing infrastructure, and an that of a drone.The frames from each video capture have been synchronized in time and all road users have been carefully annotated down to pixellevel accuracy.The dataset consists of 3100 frames from each viewpoint, containing 18883 individual annotations on the pole viewpoint, and 50274 individual annotations on the drone viewpoint.The dataset is freely available online*.Apart from the dataset, which is our main contribution, we also provide benchmark detection results for four different groups of road users for other researchers to compare their results with.We show that the detection problem is challenging, as we achieve mAPs of only 22.62% and 27.75% using a pretrained state-of-the-art detector on the two viewpoints.
Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.
A Bangla Spam Email Detection and Datasets Creation Approach based on Machine Learning Algorithms

Email is one of the most imperative communication mechanisms of the 21st century. People around the world send billions of emails every single day which makes people prone to threats. Spam emails can be used for stealing things from our electric devices, blackmailing and phishing. Moreover, we receive several unwanted emails such as advertisements and offers of e-commerce websites. Due to these emails, we sometimes miss important emails. There are several approaches available for detecting spam emails in the English language and other important languages. However, there is no such spam email detection tool available in Bangla language where Bangla is one of the most spoken languages in the world and widely used on the internet nowadays. For this reason, spammers nowadays send emails in Bangla language to Bengali people so that they can avoid being filtered out which makes us vulnerable to serious threats. Therefore, we have designed and developed a spam email detection mechanism in the Bangla language. We have also constructed datasets of Bangla spam emails to train and test our system. This paper explores the use of six supervised machine learning approaches. According to the classification results, Random Forest presented the best performance with 93.60% accuracy.
Pushing the Limit of PFA: Enhanced Persistent Fault Analysis on Block Ciphers

Persistent fault analysis (PFA) is a newly proposed cryptanalysis for block ciphers. Although the injected fault is persistent during the entire encryption, the corresponding analysis is only applied to the last round in the original PFA. In this article, the enhanced PFA (EPFA) is proposed, which can push the limit of PFA by exploiting the fault leakage in deeper rounds and target to reduce the number of required ciphertexts as small as possible. EPFA is first introduced as a general method with a specific application to advanced encryption standard (AES). Then it is extended to other substitution–permutation network (SPN)-based block ciphers, such as LED and SKINNY, both of which have unique features that EPFA fits well. To improve the efficiency of EPFA, a parallel algorithm based on mixed radix numbers is developed, which fully utilizes the power of GPU. Our experimental results show that EPFA can reduce the number of required ciphertexts to be under 1000, which is only about 40% of the 2500 ciphertexts in previous PFA on AES. In contrast to the single-threaded implementation, the parallel EPFA can have a speedup roughly about 200 times.
Tawasol Symbols: Alternative Augmented Communication Pictograms to Support the Inclusion During Pandemics

Since December 2019, we lived in unique times as a result of the COVID-19 virus in the entire world. Effective communication is always essential for everyone including persons with disabilities and in particular persons with communication impairment. Thanks to Information and Communication Technologies (ICT), access to information became easier for everyone. Everyone in the world needs to understand what is happening during natural disasters and pandemic in particular. All of them need to be able to express needs, to learn, and, to access important information, in particular, for persons with communication difficulties. For them, using pictograms, called also symbols, is a way to communicate regardless of the person's language or literacy skills (writing and reading). Therefore, they may benefit from using pictograms to communicate, express their needs, and to access education and culture domains. This paper presents an overview of Mada's experience on how to train the Alternative Augmented Communication pictograms during the pandemic and natural disasters using Tawasol Symbols (AAC), and to highlight the key findings of the initiative. In addition, the implementation of several platforms to support persons with disabilities will be showcased toward enabling their inclusion in society.
QueryBlazer: Efficient Query Autocompletion Framework

Query autocompletion is an essential feature in search engines that predicts and suggests query completions to a user's incomplete prefix input, a critical feature to enhance the user experience. While a generic lookup-based system can provide completions with great efficiency, it is unable to address prefixes not seen in the past. On the other hand, a generative system can complete unseen queries with superior accuracy but requires substantial computational overhead at runtime, making it costly for a large-scale system. Here, we present an efficient, fully-generative query autocompletion framework. Our framework employs an n-gram language model at a subword-level and exploits the n-gram model's inherent data structure to precompute completions prior to runtime. Evaluation results on public dataset show that our framework is not only as effective as previous systems with neural language models, but also reduces computational overhead at runtime, expediting the speed by more than two orders of magnitude. The goal of this work is to showcase a generative query completion system that is an attractive choice for large-scale deployments.
A design methodology for deep reinforcement learning in autonomous systems

Autonomous systems such as mobile robots will play an important role in fields like industrial production, transportation or in hostile environments such as space. One of the most fundamental problem in autonomous mobile robotics is autonomous navigation. It is imperative for a mobile robot to learn to navigate in complex environments such as roads or buildings. The most popular approach to this problem is to utilize different algorithms for mapping the environment, self-localization in the map, planning a trajectory to the given goal and executing this trajectory. However, there are some drawbacks of these approaches. We often make assumptions about the environment such as no dynamic or transparent objects. Moreover, there is considerable overhead, they do not learn from failures and operation scenarios. This prompts us to search for alternative approaches for autonomous navigation, such as deep reinforcement learning. However, the application of deep reinforcement learning to a particular task involves a series of non-trivial design decisions. Previous work have failed to address the need for a design methodology for deep reinforcement learning systems. In this paper, we propose design methodology and discuss relevant design decisions for deep reinforcement learning in autonomous systems. We apply the methodology to the problem of autonomous navigation.
Underwater image enhancement algorithm based on Retinex and wavelet fusion

Abstract Aiming at the problems of color cast, low contrast and fuzz in the original underwater image, this paper proposes an underwater image enhancement algorithm based on Retinex and wavelet fusion. First, this paper uses the multi-scale Retinex algorithm to perform color correction processing on underwater images. Then, this paper performs gamma transformation and sharpening on the image to improve the contrast and sharpness of the image. Finally, this paper uses wavelet fusion algorithm for image fusion to obtain underwater images with clear textures and details. Through subjective and objective evaluation with other algorithms, the algorithm proposed in this paper improves the color cast and fuzz, and improves the contrast and clarity of the image.
Web-based multi-user concurrent job scheduling system on the shared computing resource objects

In this paper we propose a user-friendly jobs and resources allocation manager for the ML server. We introduce some unique features of the designed system such as a protection of user’s sensitive data, automatic cleaning of unused information, secure of the host OS via environment virtualization (container), and direct access to the container via SSH. Proposed web-based tool allows users to request and allocate resources on a server and monitor the progress of their tasks. It is created to simplify access to servers particularly ML servers, to allocate computational resources while satisfying data security concerns. The proposed tool also relieves system administrators form manually allocating resources to users and monitor the progress. The tool is user friendly and transparent so that the system administrator and the user can simply view all jobs in progress to find the best allocation for their tasks. The implementation code, deployment instructions, and supplementary files are available at https://github.com/UAEUIRI/DGX1-scheduler.
On-chip tunable SOI interferometer for quantum random number generation based on phase diffusion in lasers

Quantum random number generators (QRNG) derive randomness from quantum mechanical processes and therefore are considered a reliable source of randomness. However large size, high cost and instability issues due to conventional bulk, fiber or free space implementations restrict widespread use into complex systems and significantly affect the scalability and reliability of such solutions. Recently there has been a huge interest in photonic integration technologies for quantum optics in order to overcome aforementioned issues. We report demonstration of a QRNG based on phase diffusion in gain switched (GS) lasers. It produces random numbers by translating random phases of GS pulses (generated at 2.5 GHz rate) into amplitude fluctuations using a packaged on-chip silicon on insulator (SOI) tunable unbalanced Mach–Zehnder interferometer (uMZI). The 400 ps delay line in uMZI has been specially designed by using the combination of multi-mode and single mode waveguides to reduce the losses. The combination of tunable loss control (balancing), low waveguide losses and shorter delay improve interference quality and increase random number generation rates to gigabit scale. The mid-way and output monitoring ports on the chip can potentially be used for health checks to improve the long-term stability and reliability of QRNG in line with NIST recommendations for entropy source. True random numbers are extracted by offline post processing of the raw data. The extracted random numbers show low correlation coefficients and passed all NIST randomness tests achieving 10 Gb/s generation rate.
Real-Time Bird’s Eye View Multi-Object Tracking system based on Fast Encoders for Object Detection

This paper presents a Real-Time Bird's Eye View Multi Object Tracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for object detection and a combination of Hungarian algorithm and Bird's Eye View (BEV) Kalman Filter, respectively used for data association and state estimation. The system is able to analyze 360 degrees around the ego-vehicle as well as estimate the future trajectories of the environment objects, being the essential input for other layers of a self-driving architecture, such as the control or decision-making. First, our system pipeline is described, merging the concepts of online and real-time DATMO (Deteccion and Tracking of Multiple Objects), ROS (Robot Operating System) and Docker to enhance the integration of the proposed MOT system in fully-autonomous driving architectures. Second, the system pipeline is validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, a comparison of our proposal with other state-of-the-art approaches is carried out in terms of performance by using the mainstream metrics used on MOT benchmarks and the recently proposed integral MOT metrics, evaluating the performance of the tracking system over all detection thresholds.