Reviewerly/exHarmony huggingface dataset:
Momentum-Space Decoherence of Distinguishable and Identical Particles in the Caldeira–Leggett Formalism

In this work, momentum-space decoherence using minimum and nonminimum-uncertainty-product (stretched) Gaussian wave packets in the framework of Caldeira-Leggett formalism and under the presence of a linear potential is studied. As a dimensionless measure of decoherence, purity, a quantity appearing in the definition of the linear entropy, is studied taking into account the role of the stretching parameter. Special emphasis is on the open dynamics of the well-known cat states and bosons and fermions compared to distinguishable particles. For the cat state, while the stretching parameter speeds up the decoherence, the external linear potential strength does not affect the decoherence time; only the interference pattern is shifted. Furthermore, the interference pattern is not observed for minimum-uncertainty-product-Gaussian wave packets in the momentum space. Concerning bosons and fermions, the question we have addressed is how the symmetry of the wave functions of indistinguishable particles is manifested in the decoherence process, which is understood here as the loss of being indistinguishable due to the gradual emergence of classical statistics with time. We have observed that the initial bunching and anti-bunching character of bosons and fermions, respectively, in the momentum space are not preserved as a function of the environmental parameters, temperature, and damping constant. However, fermionic distributions are slightly broader than the distinguishable ones and these similar to the bosonic distributions. This general behavior could be interpreted as a residual reminder of the symmetry of the wave functions in the momentum space for this open dynamics.
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation

To find a suitable embedding for a knowledge graph remains a big challenge nowadays.By using previous knowledge graph embedding methods, every entity in a knowledge graph is usually represented as a k-dimensional vector.As we know, an affine transformation can be expressed in the form of a matrix multiplication followed by a translation vector.In this paper, we firstly utilize a set of affine transformations related to each relation to operate on entity vectors, and then these transformed vectors are used for performing embedding with previous methods.The main advantage of using affine transformations is their good geometry properties with interpretability.Our experimental results demonstrate that the proposed intuitive design with affine transformations provides a statistically significant increase in performance with adding a few extra processing steps or adding a limited number of additional variables.Taking TransE as an example, we employ the scale transformation (the special case of an affine transformation), and only introduce k additional variables for each relation.Surprisingly, it even outperforms RotatE to some extent on various data sets.We also introduce affine transformations into RotatE, Distmult and ComplEx, respectively, and each one outperforms its original method.
REVOLUSI DIGITAL &amp; DINAMIKA PERKEMBANGAN CRYPTOCURRENCY DITINJAU DARI PERSPEKTIF LITERATUR REVIEW

The spread of digital technology currently faster and influence on all of human dimension, including to the financial sector. The presence of cryptocurrency using blockchain technology has changed the previously system that has been used for a long period and it come a pros and cons. The system built using peer-to-peer cause the system no longer need the 3rd party that control the financial system and since the system decentralized. The nature of cryptocurrency which made as the same with gold characteristic such as the limitation of quantity, process to get it must through the mining process, more resistance from inflation are making people and society interested and significantly increase time by time. The decentralized and anonymous blockchain working system for token holders and safe has presented as a new paradigm in the financial sector. But on other hand, there are many challenges and dynamics that arise due to the emergence of these cryptocurrency, one of which is risk from the legal perspective where there are cases of money laundering involving of crypto, losses to the investor asset due to ICO process that not run smoothly, the emergence losses from crypto exchanges that hacked due to not have a high security backup system that made the customer losses their digital wallet. Crypto value that tend to have high volatility and fluctuating rates with fast count also increase the risk. The readiness of countries and drafting laws related to the crypto was contribution for this research. One of the biggest interested from the public is the use of crypto as long asset investment, where the values increasing from year to year, even Bitcoin as one of the cryptocurrencies that previously worthless but currently has very fantastic values and become a favourite from the investor
Prediction of The Level of Public Trust in Government Policies in the 1<sup>st</sup> Quarter of The Covid 19 Pandemic using Sentiment Analysis

The covid-19 pandemic has made changes in society, including Government policy. The policy changes led to mixing responses from the public, namely netizens. Netizen shares their opinion in social media, including Twitter. Their opinion can represent the public’s trust in the Government. Sentiment analysis analyses others’ opinions and categorises them into positive opinions, negative opinions, or neutral opinions. Sentiment analysis can analyze large numbers of opinions so that public opinion can be analyzed quickly. This paper explains how to analyze public trust using sentiment analysis and to use Naïve Bayes classification method to analyze sentiment. The data research was taken from Twitter in the first quarter of the Covid-19 pandemic, with around 3000 tweets. The tweets were related to Covid-19 and the Government from several countries such as the United States, Australia, Ireland, Switzerland, Italy, Philippines, Sri Lanka, Canada, Netherlands, United Kingdom, Germany, and Lebanon. This study aims to determine the level of public trust in the Government in the first quarter of the Covid-19 pandemic. The research result is expected to be used as a reference for the public policy stakeholders to determine future policies.
Claim Verification using a Multi-GAN based Model

This article describes research on claim verification carried out using a multiple GAN-based model.The proposed model consists of three pairs of generators and discriminators.The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels.A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model.The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data.The synthetically generated data helps to gain information that improves classification performance over state of the art baselines.The respective F1 scores after applying the proposed method on FEVER 1.0 and FEVER 2.0 datasets are 0.65±0.018and 0.65±0.051.
The Effect of Neurological Impress Method (N.I.M) on Reading Ability of English Text on Dyslexic Students

This study aims to find out the effect of the neurological impress method on the reading ability of English text on dyslexic students, including phonemic awareness, reading fluency, and reading comprehension. This study intends to determine the use of the neurological impress method to boost the reading ability of dyslexic students in Galuh Handayani Inclusive School Surabaya. The participants of this study are three dyslexic students and two teachers. The data were collected through observation and interview and analyzed by using the descriptive qualitative method. In short, this study supports the use of the neurological impress method to develop the reading ability of English text as a foreign language on students with learning disabilities. According to the observation and interview that have done by the researcher in Galuh Handayani Inclusive School Surabaya, it can conclude that the neurological impress method is the method that appropriates for increasing the reading ability of dyslexic students.
Python Mini-Tutorial

For the Python examples discussed in this book, we used Anaconda Python 2.7 or 3.6. We expect that any other Python distribution should work equally well.
Two-Face: Adversarial Audit of Commercial Face Recognition Systems

Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance. Audits of these applications have revealed that they tend to be biased against minority groups which result in unfair and concerning societal and political outcomes. Despite multiple studies over time, these biases have not been mitigated completely and have in fact increased for certain tasks like age prediction. While such systems are audited over benchmark datasets, it becomes necessary to evaluate their robustness for adversarial inputs. In this work, we perform an extensive adversarial audit on multiple systems and datasets, making a number of concerning observations- there has been a drop in accuracy for some tasks on CelebSET dataset since a previous audit. While there still exists a bias in accuracy against individuals from minority groups for multiple datasets, a more worrying observation is that these biases tend to get exorbitantly pronounced with adversarial inputs toward the minority group. We conclude with a discussion on the broader societal impacts in light of these observations and a few suggestions on how to collectively deal with this issue.
Flexible Generation of Natural Language Deductions

An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form. Natural language is an attractive representation for this purpose — it is both highly expressive and easy for humans to understand. However, manipulating natural language statements in logically consistent ways is hard: models must cope with variation in how meaning is expressed while remaining precise. In this paper, we describe ParaPattern, a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision. We train BART-based models (Lewis et al., 2020) to generate the result of applying a particular logical operation to one or more premise statements. Crucially, we develop a largely automated pipeline for constructing suitable training examples from Wikipedia. We evaluate our models using out-of-domain sentence compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et al., 2021) datasets as well as targeted perturbation sets. Our results show that our models are substantially more accurate and flexible than baseline systems. ParaPattern achieves 85% validity on examples of the ‘substitution’ operation from EntailmentBank without the use of any in-domain training data, matching the performance of a model fine-tuned for EntailmentBank. The full source code for our method is publicly available.
Supply Chain Credit Evaluation Mechanism Integrating Federated Learning and Blockchain

Since the financing of small and medium-sized enterprises in the supply chain has been difficult to solve, more and more people have begun to pay attention to the field of supply chain finance. Federated learning, as a method that can effectively protect privacy, can also be effectively used in solving the financing problems of supply chain enterprises. At the same time, blockchain technology can effectively guarantee the credibility, openness and transparency of information, so this paper proposes a supply chain credit evaluation mechanism that integrates federated learning and blockchain. In this mechanism, we can ensure that the private data of supply chain enterprises can be used without going out of the local area, and we can also provide a blockchain record that guarantees the smooth progress of the financing process.
Content Based Video Retrieval—Methods, Techniques and Applications

Videos are rich information sources than individual images, they are considered as most influential communication media compared to others. The amount of video data produced and dispensed are growing exponentially day by day with the availability of electronic media such as smart phones, handicams etc. and broadband services at cheaper rates, as well as easy accessibility of those media in the market. Video data storage and access founds its applications in different fields such as digital libraries, video on demand, entertainment etc. and these applications are popular and needs regular access of videos from the libraries. All the above said compound reasons demanded the need of development of efficient video management and retrieval systems which can efficiently retrieve videos similar to the query as well as with a less response time. Video retrieval is made possible by searching of the desired video through a user demanded query. The user inputted query may be in the form of representative keywords or a single image or group of images. The video retrieval systems are classified as text based or content based, according to the query inputted by the user. In a text based video retrieval system query is in the form of representative keywords and the database videos are tagged with appropriate text. An example of concept based search and retrieval system is YouTube. The principal drawback in concept based system is mapping of high level or rich semantics to low level features, which is known as semantic gap. Another drawback in concept based video retrieval systems is intention gap, which denotes gap between query at querying time and intention of the search. Several researchers found content based video retrieval (CBVR) system as solution to the drawbacks of a concept based video retrieval system. The main objective this chapter is to provide comprehensive outlook on content based video retrieval (CBVR) system and its recent developments and a new content based video retrieval system that is going to be developed by feature fusion. The generalized algorithm of CBVR and its individual stages such as keyframe extraction and feature extraction also will be described elaborately. This chapter focuses on a brief overview of CBVR, keyframe extraction, feature extraction and feature fusion.
Distributed Reconnaissance Task Planning for Multi-UAV System

With the increasing complexity of reconnaissance task and the rapid development of multiple unmanned aerial vehicle (UAV), autono-mous task planning has become a key technology for military multi-UAV system. Aiming to solve the multi-task reconnaissance planning by multi-UAV, a novel distributed task planning method is proposed in this paper. Firstly, a distributed contract network protocol is proposed to achieve task allocation for multi-UAV. Comparing with traditional method, the communication load is reduced and the allocation speed is improved. Afterwards, an improved PSO path planning is carried out to provide a feasible flying path to the target. The planning efficiency is increased via introducing chaotic map initialization and adaptive parameter adjustment. Finally, numerical simulations for multi-task reconnaissance mission are given. The results show the quick generation of task execution sequence and reconnaissance paths, and are also consistent with reconnaissance engineering applications.
Automated Digital Notification of COVID-19 Diagnoses Through Text and Email Messaging — North Carolina, December 2020–January 2021

During October 3, 2020-January 9, 2021, North Carolina experienced a 400% increase in daily reported COVID-19 cases (1). To handle the increased number of cases and rapidly notify persons receiving a positive SARS-CoV-2 test result (patients), North Carolina state and local health departments moved from telephone call notification only to telephone call plus automated text and email notification (digital notification) beginning on December 24, 2020. Overall, among 200,258 patients, 142,975 (71%) were notified by telephone call or digital notification within the actionable period (10 days from their diagnosis date)* during January 2021, including at least 112,543 (56%) notified within 24 hours of report to North Carolina state and local health departments, a significantly higher proportion than the 25,905 of 175,979 (15%) notified within 24 hours during the preceding month (p<0.001). Differences in text notification by age, race, and ethnicity were observed. Automated digital notification is a feasible, rapid and efficient method to support timely outreach to patients, provide guidance on how to isolate, access resources, inform close contacts, and increase the efficiency of case investigation staff members.
Assessment of Deep Learning Methods for Differentiating Autoimmune Disorders in Ultrasound Images.

At present, deep learning becomes an important tool in medical image analysis, with good performance in diagnosing, pattern detection, and segmentation. Ultrasound imaging offers an easy and rapid method to detect and diagnose thyroid disorders. With the help of a computer-aided diagnosis (CAD) system based on deep learning, we have the possibility of real-time and non-invasive diagnosing of thyroidal US images. This paper proposed a study based on deep learning with transfer learning for differentiating the thyroidal ultrasound images using image pixels and diagnosis labels as inputs. We trained, assessed, and compared two pre-trained models (VGG-19 and Inception v3) using a dataset of ultrasound images consisting of 2 types of thyroid ultrasound images: autoimmune and normal. The training dataset consisted of 615 thyroid ultrasound images, from which 415 images were diagnosed as autoimmune, and 200 images as normal. The models were assessed using a dataset of 120 images, from which 80 images were diagnosed as autoimmune, and 40 images diagnosed as normal. The two deep learning models obtained very good results, as follows: the pre-trained VGG-19 model obtained 98.60% for the overall test accuracy with an overall specificity of 98.94% and overall sensitivity of 97.97%, while the Inception v3 model obtained 96.4% for the overall test accuracy with an overall specificity of 95.58% and overall sensitivity of 95.58.
Security Enhancement of RSA Algorithm using Increased Prime Number Set

In this era of digital age a lot of secret and non-secret data is transmitted over the internet. Cryptography is one of the many techniques to secure data on network. It is one of the techniques that can be used to ensure information security and data privacy. It is used to secure data in rest as well as data in transit. RSA in the most commonly used cryptographic algorithm and it is also used for the creation on Digital Certificates. RSA algorithm is now not considered to be as secure due to advancement in technology and newer attack vectors. This paper proposed an algorithm for security enhancement of RSA algorithm by increasing prime numbers count. Proposed algorithm has been implemented to encrypt and decrypt the data and execution results for encryption and decryption time have been compared for increased prime numbers count. This proposed algorithm of RSA can be used to replace the existing RSA algorithm in digital signature certificates as well as in all other places where the base RSA algorithm is currently being used. In the proposed technique, as the number of prime number count increases, prime factor calculation becomes difficult. If the attacker has encryption key (e) and Product of prime numbers (N) then it is not easy to find out the prime number combinations and hence decryption key (d) will be more secure by using proposed algorithm. This will be more difficult because given a number n, it is easy to find two numbers whose product is equal to n using Shor's algorithm and Grover’s Search Algorithm but it is not very difficult and time taking to exactly determine m numbers whose product is equal to n.
Nearly Horizon-Free Offline Reinforcement Learning

We revisit offline reinforcement learning on episodic time-homogeneous tabular Markov Decision Processes with $S$ states, $A$ actions and planning horizon $H$. Given the collected $N$ episodes data with minimum cumulative reaching probability $d_m$, we obtain the first set of nearly $H$-free sample complexity bounds for evaluation and planning using the empirical MDPs: 1.For the offline evaluation, we obtain an $\tilde{O}\left(\sqrt{\frac{1}{Nd_m}} \right)$ error rate, which matches the lower bound and does not have additional dependency on $\poly\left(S,A\right)$ in higher-order term, that is different from previous works~\citep{yin2020near,yin2020asymptotically}. 2.For the offline policy optimization, we obtain an $\tilde{O}\left(\sqrt{\frac{1}{Nd_m}} + \frac{S}{Nd_m}\right)$ error rate, improving upon the best known result by \cite{cui2020plug}, which has additional $H$ and $S$ factors in the main term. Furthermore, this bound approaches the $\Omega\left(\sqrt{\frac{1}{Nd_m}}\right)$ lower bound up to logarithmic factors and a high-order term. To the best of our knowledge, these are the first set of nearly horizon-free bounds in offline reinforcement learning.
Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.
A COMPARATIVE STUDY OF COMMON EDGE DETECTION OPERATORS IN DIGITAL IMAGE PROCESSING

Edge detection is a fundamental process in image processing that extracts information about the image and facilitates image segmentation and feature extraction.It has many applications in various fields of computer vision.Thus, it is very necessary to understand the performance of each of these edge detectors.This paper presents a comparative study of common edge detection operators in image processing using mean squared error (MSE), peak signal to noise ratio (PSNR), and Execution time (Et).The paper shows the canny edge detector is computationally expensive but provides higher accuracy in edge detection with higher PSNR and lower MSE.The software tool used in the project is MATLAB SIMULINK R2020a.