Reviewerly/exHarmony huggingface dataset:
Python NLTK Sentiment Inspectionusing Naïve Bayes Classifier

The Web is one of the richest sources for gathering of consumer reviews and opinions. There are many websites which contains opinions of the customers in the form of reviews, blogs, discussion groups, and forums. This project focuses on customer reviews on the restaurants. It predicts whether the given comment is either a positive or negative using supervised machine learning techniques. The project makes use of a dataset from Kaggle website. The dataset consists of comment and the type of comment (i.e., either positive or negative). This project makes a study on classification algorithm and text mining approaches to identify the type of comment. Firstly, the data set which is taken is made free from duplicates. That is duplicates are removed then it is followed by text pre-processing that involves removal of punctuation marks, stop word removal and then conversion of the whole text into vector format would takes place. The conversion from text to vector is an essential step because the English cannot be directly used for the analysis as we are working with linear algebra. So, as to work with this data, it has to be converted to vector format and we are using CountVectorizer to convert the data to the vector format. And finally comes the classification part. We are using Naive Bayes algorithm for this classification. This classification makes the data set into two parts as mentioned above. Here we are taking 70 percent of the data to be train data set and 30 percent of the data to be test data set
An Execution of a Mathematical Example Using Euler’s Phi-Function in Hill Chiper Cryptosystem

In this article, we have explained the RSA public-key cryptosystem initiate by Rivest and Hill Chiper cryptosystem initiate by Lester. Hill for coding and decoding the text. To explain these we apply the Euler-phi function, congruence, and simple Matrix Application in cryptography to decode and encode the message. In our analyses, we have collaborated two cryptosystems which is more assured than standard cryptographic process such as Ceaser cipher. We apply both secret-key cryptography and public-key cryptography which differ from standard cryptography. In our presentation, we use two keys for coding and two for decoding.
CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

The problem of medical data classification is analyzed and the methods of classification are reviewed in various aspects. However, the efficiency of classification algorithms is still under question. With the motivation to leverage the classification performance, a Class Level disease Convergence and Divergence (CLDC) measure based algorithm is presented in this paper. For any dimension of medical data, it convergence or divergence indicates the support for the disease class. Initially, the data set has been preprocessed to remove the noisy data points. Further, the method estimates disease convergence/divergence measure on different dimensions. The convergence measure is computed based on the frequency of dimensional match where the divergence is estimated based on the dimensional match of other classes. Based on the measures a disease support factor is estimated. The value of disease support has been used to classify the data point and improves the classification performance.
Hybrid Weed-Particle Swarm Optimization Algorithm and CMixture for Data Publishing

From the experts and researchers, data publishing is the center of attention in the latest technology, which receives great interest.The idea of data publishing faces a large number of security problems chiefly, while any trusted organization presents data to the third party, personal information requires not to be revealed.Hence, to keep the data privacy, this work presents a method for privacy preserved collaborative data publishing by exploiting the Weed and Particle Swarm Optimization algorithm (W-PSO) for that a C-mixture parameter is utilized.The parameter of C-mixture improves data privacy if the data does not assure privacy constraints, like l -diversity, m -privacy and k -anonymity.The least fitness value is controlled which is based upon the least value of the widespread information loss and the least value of the average equivalence class size.The minimum value of the fitness assures the utmost utility and the least privacy.Simulation is performed by exploiting the adult dataset and the proposed method is superior to the conventional algorithms regarding the widespread information loss and the average equivalence class metric and attained minimum values.
Reading Format Attitudes in the Time of COVID

The objective of this paper is to investigate the impact of the COVID-19-related changes to educational delivery on students' academic reading format preferences and behaviors. Pre-pandemic studies showed that students preferred print when needing to engage in academic texts for their coursework, and that under certain circumstances, students learned better when using print texts. During the pandemic however, many institutions implemented a sudden shift to remote learning and electronic readings. We questioned whether students would adjust their learning strategies to accommodate the abrupt change, and whether the increased experience with e-formats would boost their favorable attitudes towards digital reading. This study's data from students at a North American university does not support this hypothesis. While some respondents did report improved attitudes towards e-reading during COVID, most attitudes were less favorable or reflected no change. Nearly half the respondents stated that they highlighted and annotated their readings less than they did before, and over a third said they completed their assigned readings less frequently. Negative feelings may reflect a new trend in attitudes or they may be a temporary outcome of general screen fatigue. The findings reinforce the importance of providing print format options for students during this time.
Large-scale extraction of interpretable features provides new insights into kidney histopathology – A proof-of-concept study

Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We define feature selection methods to extract the most informative and discriminative features. We perform statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. Particularly for the presented case-study, we highlight features that are affected in each compartment. With this, prior biological knowledge, such as the increase in interstitial nuclei, is confirmed and presented in a quantitative way, alongside with novel findings, like color and intensity changes in glomeruli and Bowman's capsule. The proposed approach is therefore an important step towards quantitative, reproducible, and rater-independent analysis in histopathology.
Remote preparation and manipulation of squeezed light

Remote state preparation enables one to create and manipulate a quantum state based on the shared entanglement between distant nodes. Here, we experimentally demonstrate remote preparation and manipulation of squeezed light. By performing a homodyne projective measurement on one mode of the continuous variable entangled state at Alice's station, a squeezed state is created at Bob's station. Moreover, rotation and displacement operations are applied on the prepared squeezed state by changing the projective parameters on Alice's state. We also show that the remotely prepared squeezed state is robust against loss and N - 1 squeezed states can be remotely prepared based on an N-mode continuous variable Greenberger-Horne-Zeilinger-like state. Our results verify the entanglement-based model used in security analysis of quantum key distribution with continuous variables and have potential application in remote quantum information processing.
EyeNeRF

A unique challenge in creating high-quality animatable and relightable 3D avatars of people is modeling human eyes. The challenge of synthesizing eyes is multifold as it requires 1) appropriate representations for the various components of the eye and the periocular region for coherent viewpoint synthesis, capable of representing diffuse, refractive and highly reflective surfaces, 2) disentangling skin and eye appearance from environmental illumination such that it may be rendered under novel lighting conditions, and 3) capturing eyeball motion and the deformation of the surrounding skin to enable re-gazing. These challenges have traditionally necessitated the use of expensive and cumbersome capture setups to obtain high-quality results, and even then, modeling of the eye region holistically has remained elusive. We present a novel geometry and appearance representation that enables high-fidelity capture and photorealistic animation, view synthesis and relighting of the eye region using only a sparse set of lights and cameras. Our hybrid representation combines an explicit parametric surface model for the eyeball with implicit deformable volumetric representations for the periocular region and the interior of the eye. This novel hybrid model has been designed to address the various parts of that challenging facial area - the explicit eyeball surface allows modeling refraction and high-frequency specular reflection at the cornea, whereas the implicit representation is well suited to model lower-frequency skin reflection via spherical harmonics and can represent non-surface structures such as hair or diffuse volumetric bodies, both of which are a challenge for explicit surface models. We show that for high-resolution close-ups of the eye, our model can synthesize high-fidelity animated gaze from novel views under unseen illumination conditions.
Surgical-VQA: Visual Question Answering in Surgical Scenes Using Transformer

Visual question answering (VQA) in surgery is largely unexplored. Expert surgeons are scarce and are often overloaded with clinical and academic workloads. This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures. At times, students and junior residents also refrain from asking too many questions during classes to reduce disruption. While computer-aided simulators and recording of past surgical procedures have been made available for them to observe and improve their skills, they still hugely rely on medical experts to answer their questions. Having a Surgical-VQA system as a reliable ‘second opinion’ could act as a backup and ease the load on the medical experts in answering these questions. The lack of annotated medical data and the presence of domain-specific terms has limited the exploration of VQA for surgical procedures. In this work, we design a Surgical-VQA task that answers questionnaires on surgical procedures based on the surgical scene. Extending the MICCAI endoscopic vision challenge 2018 dataset and workflow recognition dataset further, we introduce two Surgical-VQA datasets with classification and sentence-based answers. To perform Surgical-VQA, we employ vision-text transformers models. We further introduce a residual MLP-based VisualBert encoder model that enforces interaction between visual and text tokens, improving performance in classification-based answering. Furthermore, we study the influence of the number of input image patches and temporal visual features on the model performance in both classification and sentence-based answering.
Conflict-Based Steiner Search for Multi-Agent Combinatorial Path Finding

Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations.This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations.This problem involves not only planning collisionfree paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e.multi-target sequencing).To solve the problem, we leverage the well-known Conflict-Based Search (CBS) for MAPF and propose a novel framework called Conflict-Based Steiner Search (CBSS).CBSS interleaves (1) the conflict resolving strategy in CBS to bypass the curse of dimensionality in MAPF and ( 2) multiple traveling salesman algorithms to handle the combinatorics in multi-target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets.Our extensive tests verify the advantage of CBSS over baseline approaches in terms of computing shorter paths and improving success rates within a runtime limit for up to 20 agents and 50 targets.We also evaluate CBSS with several MCPF variants, which demonstrates the generality of our problem formulation and the CBSS framework.
The Influence of Human Aspects on Requirements Engineering-related Activities: Software Practitioners’ Perspective

Requirements Engineering (RE)-related activities require high collaboration between various roles in software engineering (SE), such as requirements engineers, stakeholders, developers, and so on. Their demographics, views, understanding of technologies, working styles, communication and collaboration capabilities make RE highly human-dependent. Identifying how “human aspects” —such as motivation, domain knowledge, communication skills, personality, emotions, culture, and so on—might impact RE-related activities would help us improve RE and SE in general. This study aims at better understanding current industry perspectives on the influence of human aspects on RE-related activities, specifically focusing on motivation and personality, by targeting software practitioners involved in RE-related activities. Our findings indicate that software practitioners consider motivation, domain knowledge, attitude, communication skills and personality as highly important human aspects when involved in RE-related activities. A set of factors were identified as software practitioners’ key motivational factors when involved in RE-related activities, along with important personality characteristics to have when involved in RE. We also identified factors that made individuals less effective when involved in RE-related activities and obtained some feedback on measuring individuals’ performance when involved in RE. The findings from our study suggest various areas needing more investigation, and we summarise a set of key recommendations for further research.
Exploring Language Hierarchy for Video Grounding

The understanding of language plays a key role in video grounding, where a target moment is localized according to a text query. From a biological point of view, language is naturally hierarchical, with the main clause (predicate phrase) providing coarse semantics and modifiers providing detailed descriptions. In video grounding, moments described by the main clause may exist in multiple clips of a long video, including both the ground-truth and background clips. Therefore, in order to correctly discriminate the ground-truth clip from the background ones, this co-existence leads to the negligence of the main clause, and concentrate the model on the modifiers that provide discriminative information on distinguishing the target proposal from the others. We first demonstrate this phenomenon empirically, and propose a Hierarchical Language Network (HLN) that exploits the language hierarchy, as well as a new learning approach called Multi-Instance Positive-Unlabelled Learning (MI-PUL) to alleviate the above problem. Specifically, in HLN, the localization is performed on various layers of the language hierarchy, so that the attention can be paid to different parts of the sentences, rather than only discriminative ones. Furthermore, MI-PUL allows the model to localize background clips that can be possibly described by the main clause, even without manual annotations. Therefore, the union of the two proposed components enhances the learning of the main clause, which is of critical importance in video grounding. Finally, we evaluate that our proposed HLN can plug into the current methods and improve their performance. Extensive experiments on challenging datasets show HLN significantly improve the state-of-the-art methods, especially achieving 6.15&#x0025; gain in terms of <inline-formula> <tex-math notation="LaTeX">$Recall 1\text{@}IoU0.5$ </tex-math></inline-formula> on the TACoS dataset.
Porting of Semantically Annotated and Geo-Located Images to an Interoperability Framework

The development of virtual or augmented cultural experiences in the context of digital humanities have introduced new requirements for the delivery of media contents and related meta-data through the web. One relevant requirement is the utilization of interoperability standards for delivering high-quality, attributed digital objects online at scale. Interoperability must be provided at different levels such as image visualization, presentation and search. In this paper we will present a research project that pursues the valorization of archaeological sites which are out of the main touristic itineraries by the exploitation information technologies, which allow to share by an interoperable interface their cultural assets. We describe an experience of porting of a collection of semantic annotated images from a legacy content management system to a new technological stack made of open source independent software to support the IIIF interoperability standard
Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes

Deep-learning-based device fingerprinting has recently been recognized as a key enabler for automated network access authentication. Its robustness to impersonation attacks due to the inherent difficulty of replicating physical features is what distinguishes it from conventional cryptographic solutions. Although device fingerprinting has shown promising performance, its sensitivity to changes in the network operating environment still poses a major limitation. This article presents an experimental framework that aims to study and overcome the sensitivity of LoRa-enabled device fingerprinting to such changes. We first begin by describing RF datasets we collected using our LoRa-enabled wireless device testbed. We then propose a new fingerprinting technique that exploits out-of-band distortion information caused by hardware impairments to increase the fingerprinting accuracy. Finally, we experimentally study and analyze the sensitivity of LoRa RF finger-printing to various network setting changes. Our results show that fingerprinting does relatively well when the learning models are trained and tested under the same settings. However, when trained and tested under different settings, these models exhibit moderate sensitivity to channel condition changes and severe sensitivity to protocol configuration and receiver hardware changes when IQ data is used as input. However, when FFT data is used as input, they perform poorly under any change.
Blockchain Adaptation in Healthcare: SWOT Analysis

The rapid growth of industrialization and the needs of the population impose more and more tasks on the health care system. The functions performed by the modern healthcare system today, along with diagnostics and treatment, are a reliable system for ensuring high-quality storage, transmission, and processing of patient data, quality control of medicines and medical equipment, provision of timely remote medical service, as well as the introduction of modern developing technologies. One of these technologies is Blockchain, which provides healthcare with a secure and convenient ledger for storing sensitive data. Blockchain, being a distributed encrypted database, is an irreversible, reliable storage network that meets most system requirements. However, high-energy consumption, high costs, a lack of qualified maintenance personnel, as well as a small number of proven blockchain projects that have proven the real efficiency, force us to study in more detail all the integration criteria. This paper discusses the advantages and disadvantages of blockchain adaptation in healthcare by analyzing existing use cases and built a SWOT table based on this data.
Pre-training summarization models of structured datasets for cardinality estimation

We consider the problem of pre-training models which convert structured datasets into succinct summaries that can be used to answer cardinality estimation queries. Doing so avoids per-dataset training and, in our experiments, reduces the time to construct summaries by up to 100×. When datasets change, our summaries are incrementally updateable. Our key insights are to use multiple summaries per dataset, use learned summaries for columnsets for which other simpler techniques do not achieve high accuracy, and that analogous to similar pre-trained models for images and text, structured datasets have some common frequency and correlation patterns which our models learn to capture by pre-training on a large and diverse corpus of datasets.
A Novel Enhanced Arithmetic Optimization Algorithm for Global Optimization

The arithmetic optimization algorithm (AOA) is based on the distribution character of the dominant arithmetic operators and imitates addition (A), subtraction (S), multiplication (M) and division (D) to find the global optimal solution in the entire search space.However, the basic AOA has some drawbacks of premature convergence, easily falls into a local optimal value, slow convergence rate, and low calculation precision.To improve the overall optimization ability and overcome the drawbacks of the basic AOA, an enhanced AOA (EAOA) based on the Lé vy variation and the differential sorting variation is proposed to solve the function optimization and the project optimization.The Lé vy variation increases population diversity, broadens the optimization space, enhances the global search ability and improves the calculation precision.The differential sorting variation filters out the optimal search agent, avoids search stagnation, enhances the local search ability and accelerates the convergence rate.The EAOA realizes complementary advantages of the Lé vy variation and the differential sorting variation to avoid falling into the local optimum and the premature convergence.The sixteen benchmark functions and five engineering design projects are applied to verify the effectiveness and feasibility of the EAOA.The EAOA is compared with other algorithms by minimizing the fitness value, such as artificial bee colony, ant line optimizer, cuckoo search, dragonfly algorithm, moth-flame optimization, sine cosine algorithm, water wave optimization and arithmetic optimization algorithm.The experimental results show that the overall optimization ability of the EAOA is superior to that of other algorithms, the EAOA can effectively balance the exploration and the exploitation to obtain the best solution.In addition, the EAOA has a faster convergence rate, higher calculation precision and stronger stability.