Reviewerly/exHarmony huggingface dataset:
Parallelization techniques for quantum simulation of fermionic systems

Mapping fermionic operators to qubit operators is an essential step for simulating fermionic systems on a quantum computer. We investigate how the choice of such a mapping interacts with the underlying qubit connectivity of the quantum processor to enable (or impede) parallelization of the resulting Hamiltonian-simulation algorithm. It is shown that this problem can be mapped to a path coloring problem on a graph constructed from the particular choice of encoding fermions onto qubits and the fermionic interactions onto paths. The basic version of this problem is called the weak coloring problem. Taking into account the fine-grained details of the mapping yields what is called the strong coloring problem, which leads to improved parallelization performance. A variety of illustrative analytical and numerical examples are presented to demonstrate the amount of improvement for both weak and strong coloring-based parallelizations. Our results are particularly important for implementation on near-term quantum processors where minimizing circuit depth is necessary for algorithmic feasibility.
GERAKAN LITERASI SEKOLAH TERHADAP MINAT BACA PESERTA DIDIK KELAS V SEKOLAH DASAR

In order to increase students' interest in reading, especially in elementary schools. It is undeniable that there are supporting factors and inhibiting factors during the implementation of the School Literacy Movement. The purpose of this research is to describe the implementation of the School Literacy Movement at SD Negeri 1 Nagrikidul. This type of research is qualitative with a case study design. The data collection techniques used are observation, interviews, questionnaires and documentation. The results of this study indicate that the implementation at SD Negeri 1 Nagrikidul has reached the learning stage. Supporting factors consist of the active role of all parties in the school, educators supervising literacy learning activities, a reading-friendly environment, all school parties producing written works, and support for the implementation of the School Literacy Movement at SD Negeri 1 Nagrikidul. The inhibiting factors are educators who do not make literacy learning effective, facilities and infrastructure are not comfortable, such as libraries that are not well organized and reading books in the literacy corner are also incomplete, and there is no special day for reading activities. The way to overcome this is that all schools hold a meeting regarding plans for the School Literacy Movement for the future, improve the administration of library management, add or update reading books so that it attracts students' attention to read. Based on the results of research data analysis, students' reading interest is currently in the high category. Therefore, the implementation of the School Literacy Movement in the future will remain consistent and have plans for the School Literacy Movement program so that it runs more conducive
A SURVEY OF CORRUPTION AND ANTI-CORRUPTION INITIATIVES IN AFRICA

Corruption has blighted national economies in Africa. Although most African states have subscribed to and ratified international and regional anti-corruption conventions, corruption continues unabated. Failure to incorporate treaty provisions into national law is one of the barriers to combating corruption effectively. The devastating impact of this economic crime on the lives of vulnerable groups has prompted the emergence of self-help groups intent on tackling corruption frontally. These initiatives hold much promise. However, corruption emanating from the private sector and multinational companies poses a veritable challenge that needs to be overcome.
PENERAPAN PROJECT BASED LEARNING DALAM MEMBUAT KOLASE PADA ELEMEN DASAR FASHION DESIGN

The Project Based Learning Learning Model is one model that can be applied in making collages on the Basic Elements of Fashion Design. This learning model has great potential to make students work collaboratively in producing work according to the demands of the independent curriculum. The population of this study was students at SMK Dharma Wanita Gresik, Fashion Design Skills. The purpose of this research is to find out how to apply the Project Based Learning learning model to the Basic Elements of Fashion Design in making collages, to find out the results of learning to make collages after applying the Project Based Learning learning model, to find out the level of collaboration of students in making collages through the Project Based Learning learning model. The research was carried out by observing the application of the Project Based Learning learning model, the collaborative level of students, and the assessment of learning outcomes. The application of Project Based Learning and the collaborative behavior of students was assessed by 3 observers through observations during the learning process while learning outcomes were obtained from the assessment of collage products. Data from the application of Project Based Learning gets an average score of 93.45% with very good quality, the collaborative level gets an average score of 93.05% with very good quality, and learning outcomes to make collages get an average score of 88.4. With the application of the Project Based Learning learning model in making collages, it shows that learning can be carried out at a very good collaborative level and learning outcomes achieve a minimum completeness criterion value.
SURVEY OF BLOOD CANCER DETECTION AND CLASSIFICATION TECHNIQUES

Blood cancer is a complex and heterogeneous group of diseases that can affect the production and function of blood cells. Early detection and accurate classification of blood cancer is crucial for effective treatment and improved patient outcomes. In recent years, machine learning has emerged as a promising tool for the analysis of medical data and the detection of diseases. In this paper, we present a machine learning-based approach for the detection and classification of blood cancers such as leukemia and lymphoma. We analyzed a diverse dataset of medical imaging and blood test results using various machine learning algorithms such as decision trees, random forests, support vector machines, and deep neural networks. Our results show that machine learning algorithms can effectively detect and classify blood cancers with high accuracy, outperforming traditional statistical methods. The deep neural network approach, in particular, achieved a classification accuracy of over 95%. The results of our study demonstrate the potential of machine learning in the early detection and accurate classification of blood cancers, which can aid in the development of personalized treatment plans and improve patient outcomes. In conclusion, our study highlights the potential of machine learning as a diagnostic tool for blood cancer detection and classification. However, it is important to note that machine learning should be used in conjunction with other diagnostic methods for accurate results and to account for the complex and heterogeneous nature of blood cancers. Further research is needed to validate these results in larger and more diverse patient populations. Key Words: Blood cancer, ALL, AML, CML, Machine Learning, Blood Cancer, Leukemia, Image Processing, CNN Architecture.
DETECTION OF HANDWRITTEN SIGNATURE FORGERY USING CNN

Hand verification and phony discovery is the process of vindicating autographs automatically and incontinently to determine whether the hand is real or not. There are two main kinds of hand verification static and dynamic. stationary, or offline verification is the process of vindicating a document hand after it has been made, while dynamic or online verification takes place as a person creates his/ her hand on a digital tablet or an analogous device. The hand in question is also compared to former samples of that person's hand, which set up the database. In the case of a handwritten hand on a document, the computer needs the samples to be scrutinized for disquisition, whereas a digital hand which is formerly stored in a data format can be used for hand verification. The handwritten hand is one of the most generally accepted particular attributes for verification of identity, whether it may for banking or business. The sub-sphere of machine literacy that's deep literacy allows us to train a model with the help of deep literacy algorithms. This is enforced in a complicated neural network model trained to classify and descry the forged hand from a collection of image samples which consists of two different sets of images, say real and forged image sets. We constructed a complicated neural network model from scrape to prize features from a given dataset. The image is given as input, and it tells whether a hand is forged or not. This will help descry the fraud. KEYWORDS: CNN, Forgery, Signature
Transfer learning for semantic similarity measures based on symbolic regression

Recently, transfer learning strategies have become ideal for reusing acquired knowledge through a training phase. The key idea is that reusing such knowledge brings advantages such as increased accuracy and considerable resource savings. In this work, we design a novel strategy for effective and efficient transfer learning in semantic similarity. Our approach is based on generating and transferring optimal models obtained through a symbolic regression process being able to stack evaluation scores from several fundamental techniques. After an exhaustive empirical study, the results lead to high accuracy in addition to significant savings in terms of training time consumed in most of the scenarios considered.
CRYPTOCURRENCIES: GROWTH AND MOVEMENT IN INDIA

There is a debate among investors about the benefits cryptocurrencies can take to a portfolio and how their prices move in the market.It's not hard to see that cryptocurrencies are highly volatile, but that doesn't stop investors from pouring large sums of money into cryptocurrency investments.Generate huge returns or catastrophic losses.One of the main challenges facing cryptocurrencies is determining how they perform with assets such as stocks and the rest of the market.The purpose of this article is to investigate whether cryptocurrencies provide diversification benefits and whether individual cryptocurrencies move similarly against each other.Of particular interest is whether there is a connection between the cryptocurrency market and the stock market.The cryptocurrencies chosen for the project were primarily benchmarked against stocks in the information technology-focused Nasdaq 100 Index, as well as a number of other assets.
Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution

Although automated test generation is common in many programming domains, games still challenge test generators due to their heavy randomisation and hard-to-reach program states. Neuroevolution combined with search-based software testing principles has been shown to be a promising approach for testing games, but the co-evolutionary search for optimal network topologies and weights involves unreasonably long search durations. In this paper, we aim to improve the evolutionary search for game input generators by integrating knowledge about human gameplay behaviour. To this end, we propose a novel way of systematically recording human gameplay traces, and integrating these traces into the evolutionary search for networks using traditional gradient descent as a mutation operator. Experiments conducted on eight diverse Scratch games demonstrate that the proposed approach reduces the required search time from five hours down to only 52 minutes.
Prototypical Bidirectional Adaptation and Learning for Cross-Domain Semantic Segmentation

Cross-domain semantic segmentation, which aims to address the distribution shift while adapting from a labeled source domain to an unlabeled target domain, has achieved great progress in recent years. However, most existing work adopts a source-to-target adaptation path, which often suffers from clear class mismatching or class imbalance issues. We design PBAL, a prototypical bidirectional adaptation and learning technique that introduces bidirectional prototype learning and prototypical self-training for optimal inter-domain alignment and adaptation. We perform bidirectional alignments in a complementary and cooperative manner which balances both dominant and tail categories as well as easy and hard samples effectively. In addition, We derive prototypes efficiently from a source-trained classifier, which enables class-aware adaptation as well as synchronous prototype updating and network optimization. Further, we re-examine self-training and introduce prototypical contrast above it which greatly improves inter-domain alignment by promoting better intra-class compactness and inter-class separability in the feature space. Extensive experiments over two widely studied benchmarks show that the proposed PBAL achieves superior domain adaptation performance as compared with the state-of-the-art.
Deep Global and Local Matching Network for Implicit Recommendation

In real recommendation scenarios, users often have implicit behaviors including clicks, rather than explicit behaviors. In order to solve the matching problem under implicit data, many researchers have proposed methods based on neural networks, mainly including representation learning and matching function learning methods. However, these methods do not take into account the diverse preferences of users, and there is no fine-grained modeling matching relationship. In this paper, we consider the matching problem from a new perspective and propose a novel deep global and local matching network (DeepGLM) model. In detail, DeepGLM introduces multi-aspect representations to express the user’s various preferences, and calculates the global matching degree between user and item through the hierarchical interactive matching module. Then, the attention mechanism is adopted to calculate the local matching relationship based on feature interactions. In addition, the gating mechanism is used to control the effective transmission of global and local matching information. Extensive experiments on four real-world datasets show significant improvements of our proposed model over the state-of-the-art methods.
CYBERCRIMINALS AND NIGERIAN CYBERCRIMES ACT 2015: CONCEPTUALISING COMPUTERS FOR CYBERCRIME JUSTICE

Cybercrime being an act committed through the use of the computer either as a tool or target makes the computer an indispensable tool for almost all cybercrimes. The increasing number of persons utilizing computers, the different purposes for which they are put, coupled with the intractable problems of defining what is and what is not a computer under the Nigerian Cybercrimes Act 2015 is central to the determination of guilt or otherwise of cybercriminals brought before the court for prosecution. The conceptualization of computers under the Nigerian Cybercrimes Act 2015 provides the trial judge with so much discretion and little or no guidanceas to whether a particular device is, or is not a computer. Moreover, considering the development of information and communication technology, the concept is continuously challenged.
Learning Enhanced Representations via Contrasting for Multi-view Outlier Detection

Multi-view outlier detection has attracted rapidly growing attention to researchers due to its wide applications. However, most existing methods fail to detect outliers in more than two views. Moreover, they only employ the clustering technique to detect outliers in a multi-view scenario. Besides, the relationships among different views are not fully utilized. To address the above issues, we propose ECMOD for learning enhanced representations via contrasting for multi-view outlier detection. Technically, ECMOD leverages two channels, the reconstruction and the constraint view channels, to learn the multi-view data, respectively. The two channels enable ECMOD to capture the rich information better associated with outliers in a latent space due to fully considering the relationships among different views. Then, ECMOD integrates a contrastive technique between two groups of embeddings learned via the two channels, serving as an auxiliary task to enhance multi-view representations. Furthermore, we utilize neighborhood consistency to uniform the neighborhood structures among different views. It means that ECMOD has the ability to detect outliers in two or more views. Meanwhile, we develop an outlier score function based on different outlier types without clustering assumptions. Extensive experiments on real-world datasets show that ECMOD significantly outperforms most baselines.
A Joint Learning Model for Open Set Recognition with Post-processing

In open set recognition (OSR), the model not only needs to correctly recognize known class samples, but also needs to be able to effectively reject unknown samples. To address this problem, we propose a joint learning model with post-processing based on the concept of Reciprocal Points. Specifically, to guarantee the accuracy of known class recognition, we design a two-branch network containing a self-supervised branch and a classification branch. The self-supervised branch helps the model classify known classes more accurately. Then, to avoid misjudging unknown samples as known ones with high confidence, we carefully redesign the open loss to better separate the known and unknown spaces, and design a post-processing mechanism to penalize the predictions of potential unknown samples. We perform several experiments and ablations on our model, obtaining the state-of-the-art results on most datasets for open set recognition and unknown detection tasks.
BACH: Black-Box Attacking on Deep Cross-Modal Hamming Retrieval Models

The growth of online data has increased the need for retrieving semantically relevant information from data in various modalities, such as images, text, and videos. Thanks to the powerful representation capabilities of deep neural networks (DNNs), deep cross-modal hamming retrieval (i.e., DCMHR) models have become popular in cross-modal retrieval tasks due to their efficiency and low storage cost. However, the vulnerability of DNN models makes them susceptible to small perturbations. Existing attacks on DNN models focus on supervised tasks like classification and recognition, and are not applicable to DCMHR models. To fill this gap, in this paper, we present BACH, an adversarial learning-based attack method for DCMHR models. BACH uses a triplet construction module to learn and generate well-designed adversarial samples in a black-box setting, without prior knowledge of the target models. During the learning process, we estimate the gradient of the objective function by using random gradient-free (RGF) method. To evaluate the effectiveness and efficiency of BACH, we perform thorough experiments on 3 popular cross-modal retrieval dataset and 13 state-of-the-art DCMHR models, including 6 image-to-image retrieval models and 7 image-to-text retrieval models. As a comparison, we select two established adversarial attack methods: CMLA for white-box attack and AACH for black-box attack. The results show that BACH offers comparable attack performance to CMLA while requiring no knowledge of the target models. Furthermore, BACH surpasses AACH on most DCMHR models in terms of attack success rate with limited queries.
Constrained Pseudorandom Functions from Homomorphic Secret Sharing

We propose and analyze a simple strategy for constructing 1-key constrained pseudorandom functions (CPRFs) from homomorphic secret sharing. In the process, we obtain the following contributions: first, we identify desirable properties for the underlying HSS scheme for our strategy to work. Second, we show that (most of) recent existing HSS schemes satisfy these properties, leading to instantiations of CPRFs for various constraints and from various assumptions. Notably, we obtain the first (1-key selectively secure, private) CPRFs for inner-product and (1-key selectively secure) CPRFs for $$\textsf{NC}^1$$ from the DCR assumption, and more. Last, we revisit two applications of HSS equipped with these additional properties to secure computation: we obtain secure computation in the silent preprocessing model with one party being able to precompute its whole preprocessing material before even knowing the other party, and we construct one-sided statistically secure computation with sublinear communication for restricted forms of computation.
Dynamic Augmentation Data Selection for Few-shot Text Classification

Data augmentation has been a popular method for fine-tuning pre-trained language models to increase model robustness and performance. With augmentation data coming from modifying gold train data (in-sample augmentation) or being harvested from general domain unlabeled data (out-of-sample augmentation), the quality of such data is the key to successful fine-tuning. In this paper, we propose a dynamic data selection method to select effective augmentation data from different augmentation sources according to the model's learning stage, by identifying a set of augmentation samples that optimally facilitates the learning process of the most current model. The method firstly filters out augmentation samples with noisy pseudo labels through a curriculum learning strategy, then estimates the effectiveness of reserved augmentation data by its influence scores on the current model at every update, allowing the data selection process tightly tailored to model parameters. And the two-stage augmentation strategy considers in-sample augmentation and out-of-sample augmentation in different learning stages. Experiments with both kinds of augmentation data on a variety of sentence classification tasks show that our method outperforms strong baselines, proving the effectiveness of our method. Analysis confirms the dynamic nature of the data effectiveness and the importance of model learning stages in utilization of augmentation data.
EFIKASI DIRI MAHASISWA DALAM PENCARIAN INFORMASI DI WEBSITE PADA PERKULIAHAN ONLINE

The COVID-19 pandemic caused learning to be done with an online system. Although much of the current literature recognizes the ability of the Internet as a medium of learning, less is known about students' self-confidence to use the Web, especially those who take part in online learning. This research is intended to understand students' abilities when using the Internet toward online information seeking. This study considers Self-Efficacy and Information Seeking Theory of online learning to examine influences of self-efficacy on behaviour toward online information seeking. Particularly, information seeking behaviour are explored as the consequences of belief in their capabilities to organize and execute the online learning. Data were collected through a quantitative study applying online questionnaire carried out in 2022, using a representative sample (N=58). Findings showed self-efficay has a strong correlation with information seeking behaviour. Self-efficacy also has a significant influence on information seeking behaviour. If self-efficacy increase, then information seeking behaviour will increase. The findings has contributed to the evolving of the Self-Efficacy and Information Seeking Theory, particularly in online learning in developing contries like Indonesia. This study provides implications to e-learning, educators, colleges, government and website system developers of online learning sites dealing with education.