Reviewerly/exHarmony huggingface dataset:
Optimized Detection of High-Dimensional Entanglement

Entanglement detection is one of the most conventional tasks in quantum information processing. While most experimental demonstrations of high-dimensional entanglement rely on fidelity-based witnesses, these are powerless to detect entanglement within a large class of entangled quantum states, the so-called unfaithful states. In this Letter, we introduce a highly flexible automated method to construct optimal tests for entanglement detection given a bipartite target state of arbitrary dimension, faithful or unfaithful, and a set of local measurement operators. By restricting the number or complexity of the considered measurement settings, our method outputs the most convenient protocol which can be implemented using a wide range of experimental techniques such as photons, superconducting qudits, cold atoms, or trapped ions. With an experimental quantum optics setup that can prepare and measure arbitrary high-dimensional mixed states, we implement some three-setting protocols generated by our method. These protocols allow us to experimentally certify two- and three-unfaithful entanglement in four-dimensional photonic states, some of which contain well above 50% of noise.
Adversarial Learning With Cost-Sensitive Classes

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This article proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between the protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called the Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach 0 while the absolute values of a few parameters are significantly larger, becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs.
A Novel Deep Learning Based Nepali Speech Recognition

Automatic speech recognition has allowed human beings to use their voices to speak with a computer interface. Nepali speech recognition involves conversion of Nepali language to corresponding text in Devanagari lipi. This work proposes a novel approach for developing Nepali Speech recognition model based using CNN-GRU. The data is collected from the Librispeech. The collected data is pre-processed and MFCC is applied on it for feature extraction. CNN-GRU model is responsible for extraction of the features and development of the acoustic model. CTC is responsible for decoding. The performance of the developed model has been assessed using Word Error Rate of the transcribed text.
Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification

Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due to the lack of fine-grained instance-wise annotations, it still easily suffer from the domain shift problem for the discrepancy between the visual representation of diversified images and the semantic representation of fixed attributes. In this paper, we propose an innovative autoencoder network by learning Aligned Cross-Modal Representations (dubbed ACMR) for GZSC. Specifically, we propose a novel Vision-Semantic Alignment (VSA) method to strengthen the alignment of cross-modal latent features on the latent subspaces guided by a learned classifier. In addition, we propose a novel Information Enhancement Module (IEM) to reduce the possibility of latent variables collapse meanwhile encouraging the discriminative ability of latent variables. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method.
OntoSpammer: A Two-Source Ontology-Based Spam Detection Using Bagging

With the advancement in technology that has made our lives far more accessible and better, is making our information insecure. Emails are considered the most reliable source for transferring messages, and this technology is hit hardest by spam. Spammers try to send unsolicited emails. Spam is one of the key to many cybercrime methods that use complex methods to trick particular victims. This chapter proposes a hybrid bagging approach by incorporating a pair of distinguished machine learning strategies namely the Support Vector Machine and Random Forest for spam email detection. We have incorporated the Dual Ontology model, i.e., domain ontology and ontology as a regular expression, to implement this model. The proposed OntoSpammer Model records accuracy and precision of 94.53% and 96.12%, respectively, and is compared and examined with benchmark and baseline approaches and is concluded to be one step up in terms of performance.
OntoIntAIC: An Approach for Ontology Integration Using Artificially Intelligent Cloud

Ontologies are pedagogical models used in knowledge engineering for knowledge representation, management, and integration. Ontologies play a vital role in representation and reasoning of domain knowledge and provisioning background knowledge for artificially intelligent application. This paper proposes a unique strategic framework to integrate ontologies using machine learning with the help of the artificially intelligent cloud which provides enormous computational power and reduces the processing time. First, the existing domain ontologies and contents of the World Wide Web and summarized e-book contents are fed into the cloud, where the preprocessing is done and the data is sampled using mappers and mergers and fed for classification using random forest. The yielded content is used to create a thesaurus where the information gain is computed and optimized using honey bee optimization algorithm. The performance is compared with the baseline approaches and models and was found to be very much superior. The proposed OntoIntAIC architecture achieved an accuracy of 97.33%.
Multimodal Visual Question Answering Using VizWiz Data; A Visual Assistant for the Blind

Visual Question Answering (VQA) is a Multimodal Interaction task between two domains, Computer vision and Natural Language Processing. The task is to develop a system that could answer a question related to an image. The task is easy for a human being, but it is difficult for a system as it involves multiple reasoning over the Image and question to get the most accurate answer. the basic step involved is to extract the image features using a Convolutional Neural Network and the question features extracted via a Recurrent Neural Network. features so extracted undergo multiple reasoning to get the most accurate answer. VQA can act as a visual assistant for the blind to know about the beautiful world around them. This work is basically to develop a VQA assistant for the blind using a real-world dataset known as VizWiz dataset originating from the blind.
Comparative Analysis of Consensus Algorithms Using a Directed Acyclic Graph Instead of a Blockchain, and the Construction of Security Estimates of Spectre Protocol Against Double Spend Attack

In recent years, the possibility of moving from a blockchain to a more general structure, which has additional capabilities, but remains resistant to major attacks, has been very actively explored. Basically, the new structure is a blockgraph (DAG), which has certain features. Various ways of such generalizations and partial analysis of their properties are offered. At the same time, the results of such research raise a large number of questions, so the field for research in this area remains extremely large, and the task of scaling the blockchain is very important. Over the last 5–7 years, many consensus protocols have been proposed on blockgraphs. It should be noted that the use of such protocols, although it leads to a significant increase in the speed of transaction processing, but carries certain risks. At present, none of these protocols have rigorous evidence of resistance to major attacks, such as double spend attack, network splitting attack, and so on. The construction of such proofs requires overcoming great analytical difficulties, even if you use a very simplified mathematical model of the network. In this section, we took the first step toward constructing consensus protocol stability estimates on block graphs by constructing upper estimates for the probability of a double spend attack on the SPECTRE consensus protocol. To increase its security, we have proposed a somewhat non-standard algorithm for accepting a transaction by a vendor: it must wait for a certain number of confirmation blocks to be created, so to speak, under its supervision. Methods have been developed to calculate the upper estimates of this probability depending on the network parameters and the number of confirmation blocks that the vendor must see before considering the transaction irreversible. For greater clarity, the corresponding numerical results are also given. An important property of the results obtained is that, in addition to the probability estimate itself, we can also calculate the required number of confirmation blocks sufficient to guarantee the irreversibility of the transaction with a probability as close to 1 as desired.
Hate Speech Detection on Social Media Using Machine Learning Algorithms

There is an enormous growth of social media which fully promotes freedom of expression through its anonymity feature. Freedom of expression is a human right but hate speech towards a person or group based on race, caste, religion, ethnic or national origin, sex, disability, gender identity, etc. is an abuse of this sovereignty. It seriously promotes violence or hate crimes and creates an imbalance in society by damaging peace, credibility, and human rights, etc. To overcome this problem, the hate speech detection model is made which will classify the speech and if the speech used by user is containing hate word, it will be detected and system will sent an alert message to user about it. In order to solve various hate speech problems we use some of the machine learning algorithms such as logistic regression and random forest. If user disrupts cyber guidelines, then strict action shall be taken and user’s account will be ban forever. This help to reduce cyber crimes in effective and efficient manner.
Algorithmic Primitives for Quantum-Assisted Quantum Control

We present two primitive algorithms to evaluate overlaps and transition matrix time series, which are then used to construct several quantum-assisted quantum control algorithms. Unlike previous approaches, our method bypasses tomographically complete measurements and instead relies solely on single qubit measurements. We analyze circuit complexity of composed algorithms and sources of noise arising from Trotterization and measurement errors.
Sentiment Analysis of Covid-19 Tweets Using Machine Learning and Natural Language Processing

Throughout the Covid-19 pandemic, social media has been flooded with dozens of important details that could illuminate the world. Twitter is a good community based on Natural Language Processing (NLP) strategies. In this project, our aim is to reflect the situation in the USA during the Covid-19 epidemic. Epidemics have become more prevalent in the region. Some states had large opposition in the past or were delayed in the event of standards, for example. Countries differed in the timeline for the establishment and enforcement of home occupation policies. Sentiment analysis is done via TextBlob, determining the magnitude of the tweets in the spectrum of good and bad. TextBlob is quite popular Python library used for the task of data processing. It has the feature of a simple API to share common language processing functions such as marking in speech, coding, sentiment analysis, separation, translation, etc. Marked details from these tweets form a visible timeline of global sentiment Covid-19. This opens the way for us to look at the public reaction to events caused by the epidemic.
LASOR: Learning Accurate 3D Human Pose and Shape via Synthetic Occlusion-Aware Data and Neural Mesh Rendering

A key challenge in the task of human pose and shape estimation is occlusion, including self-occlusions, object-human occlusions, and inter-person occlusions. The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild. In this paper, we focus on the estimation of human pose and shape in the case of inter-person occlusions, while also handling object-human occlusions and self-occlusion. We propose a novel framework that synthesizes occlusion-aware silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh renderer is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. In addition, keypoints-and-silhouette-driven training data in panoramic viewpoints are synthesized to compensate for the lack of viewpoint diversity in any existing dataset. Experimental results show that we are among the state-of-the-art on the 3DPW and 3DPW-Crowd datasets in terms of pose estimation accuracy. The proposed method evidently outperforms Mesh Transformer, 3DCrowdNet and ROMP in terms of shape estimation. Top performance is also achieved on SSP-3D in terms of shape prediction accuracy. Demo and code will be available at https://igame-lab.github.io/LASOR/.
Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation

The usefulness of deep learning models in robotics is largely dependent on the availability of training data. Manual annotation of training data is often infeasible. Synthetic data is a viable alternative, but suffers from domain gap. We propose a multi-step method to obtain training data without manual annotation effort: From 3D object meshes, we generate images using a modern synthesis pipeline. We utilize a state-of-the-art image-to-image translation method to adapt the synthetic images to the real domain, minimizing the domain gap in a learned manner. The translation network is trained from unpaired images, i.e. just requires an un-annotated collection of real images. The generated and refined images can then be used to train deep learning models for a particular task. We also propose and evaluate extensions to the translation method that further increase performance, such as patch-based training, which shortens training time and increases global consistency. We evaluate our method and demonstrate its effectiveness on two robotic datasets. We finally give insight into the learned refinement operations.
Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices

Mobile robots and many edge AI devices have a need to trade off computational power against power consumption, battery size, and time between charges. Consequently, it is common for such devices to have significantly less computational power than the powerful GPU-based systems typically used to train and evaluate deep neural networks. Object detection is a key aspect of visual perception for robots and edge devices but popular object detection architectures that run fastest on GPU based systems or that are designed to maximize mAP with large input image sizes may not scale well to edge devices. In this work we evaluate the latency and mAP of several model architectures from the YOLO and SSD families on a range of devices representative of robot and edge device capabilities. We also evaluate the effect of runtime framework and show that some unexpected large differences can be found. Based on our evaluations we propose new variations of the YOLO-LITE architecture which we show can provide increased mAP at reduced latency.
Speech Pre-training with Acoustic Piece

Previous speech pre-training methods, such as wav2vec2.0 and HuBERT, pre-train a Transformer encoder to learn deep representations from audio data, with objectives predicting either elements from latent vector quantized space or pre-generated labels (known as target codes) with offline clustering. However, those training signals (quantized elements or codes) are independent across different tokens without considering their relations. According to our observation and analysis, the target codes share obvious patterns aligned with phonemized text data. Based on that, we propose to leverage those patterns to better pre-train the model considering the relations among the codes. The patterns we extracted, called "acoustic piece"s, are from the sentence piece result of HuBERT codes. With the acoustic piece as the training signal, we can implicitly bridge the input audio and natural language, which benefits audio-to-text tasks, such as automatic speech recognition (ASR). Simple but effective, our method "HuBERT-AP" significantly outperforms strong baselines on the LibriSpeech ASR task.
A reinforcement learning-based path planning for collaborative UAVs

Unmanned Aerial Vehicles (UAVs) are widely used in search and rescue missions for unknown environments, where maximized coverage for unknown devices is required. This paper considers using collaborative UAVs (Col-UAV) to execute such tasks. It proposes to plan efficient trajectories for multiple UAVs to collaboratively maximize the number of devices to cover within minimized flying time. The proposed reinforcement learning (RL)-based Col-UAV scheme lets all UAVs share their traveling information by maintaining a common Q-table, which reduces the overall time and the memory complexities. We simulate the proposed RL Col-UAV scheme under various simulation environments with different grid sizes and compare the performance with other baselines. The simulation results show that the RL Col-UAVs scheme can find the optimal number of UAVs required to deploy for the diverse simulation environment and outperforms its counterparts in finding a maximum number of devices in a minimum time.
Towards neural architecture-aware exploration of compiler optimizations in a deep learning {graph} compiler

Deep Neural Networks (DNN) form the basis for many existing and emerging applications. Many DL compilers analyze the computation graphs and apply various optimizations at different stages. These high-level optimizations are applied using compiler passes before feeding the resultant computation graph for low-level and hardware-specific optimizations. With advancements in DNN architectures and backend hardware, the search space of compiler optimizations has grown manifolds. Also, the inclusion of passes without the knowledge of the computation graph leads to increased execution time with a slight influence on the intermediate representation. This paper presents preliminary results 1) summarizing the relevance of pass selection and ordering in a DL compiler, 2) neural architecture-aware selection of optimization passes, and 3) pruning search space for the phase selection problem in a DL compiler. We use TVM as a compiler to demonstrate the experimental results on Nvidia A100 and GeForce RTX 2080 GPUs, establishing the relevance of neural architecture-aware selection of optimization passes for DNNs DL compilers.
Feature matching for texture-less endoscopy images via superpixel vector field consistency

Feature matching is an important technology to obtain the surface morphology of soft tissues in intraoperative endoscopy images. The extraction of features from clinical endoscopy images is a difficult problem, especially for texture-less images. The reduction of surface details makes the problem more challenging. We proposed an adaptive gradient-preserving method to improve the visual feature of texture-less images. For feature matching, we first constructed a spatial motion field by using the superpixel blocks and estimated its information entropy matching with the motion consistency algorithm to obtain the initial outlier feature screening. Second, we extended the superpixel spatial motion field to the vector field and constrained it with the vector feature to optimize the confidence of the initial matching set. Evaluations were implemented on public and undisclosed datasets. Our method increased by an order of magnitude in the three feature point extraction methods than the original image. In the public dataset, the accuracy and F1-score increased to 92.6% and 91.5%. The matching score was improved by 1.92%. In the undisclosed dataset, the reconstructed surface integrity of the proposed method was improved from 30% to 85%. Furthermore, we also presented the surface reconstruction result of differently sized images to validate the robustness of our method, which showed high-quality feature matching results. Overall, the experiment results proved the effectiveness of the proposed matching method. This demonstrates its capability to extract sufficient visual feature points and generate reliable feature matches for 3D reconstruction and meaningful applications in clinical.
A Bottom-Up Model of Skill Learning

We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Clarion is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and human data is found in several respects.
A Blockchain-Based Fake Product Identification System

Counterfeit goods have become a global problem as consumers are being deceived into buying unauthentic goods with no way to validate the authenticity. Recently, blockchain has become more popular as it fosters trust between untrusting participants. This paper uses blockchain technology to combat the sale of counterfeit products. We use blockchain to allow manufacturers to add authentic product serial numbers onto the ledger; consumers can then use the serial number to verify the authenticity of a product before purchasing it. Blockchain plays a pivotal role in ensuring that data was not tampered with - creating a trusted environment.
Computer Vision and Machine Learning based Facial Expression Analysis

Human facial expression plays a pivotal role in depicting the mindset of an individual.The facial expression clearly conveys the change in mood of a person.Facial expression is considered as one of the natural means to study human mind and psyche.Hence analyzing facial expression creates great interest among researchers working in the area of computer vision.This work is focused towards analyzing and automatically classifying the facial expression depicted by a human face in a given image.To accomplish computer vision and machine learning algorithms are deployed.The facial expressions are classified into seven different expressions and results are discussed in detail