Reviewerly/exHarmony huggingface dataset:
Data Roaming and Quality Assessment for Composed Image Retrieval

The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR datasets are orders of magnitude smaller compared to other vision and language (V&L) datasets. Additionally, some of these datasets have noticeable issues, such as queries containing redundant modalities. To address these shortcomings, we introduce the Large Scale Composed Image Retrieval (LaSCo) dataset, a new CoIR dataset which is ten times larger than existing ones. Pre-training on our LaSCo, shows a noteworthy improvement in performance, even in zero-shot. Furthermore, we propose a new approach for analyzing CoIR datasets and methods, which detects modality redundancy or necessity, in queries. We also introduce a new CoIR baseline, the Cross-Attention driven Shift Encoder (CASE). This baseline allows for early fusion of modalities using a cross-attention module and employs an additional auxiliary task during training. Our experiments demonstrate that this new baseline outperforms the current state-of-the-art methods on established benchmarks like FashionIQ and CIRR.
Global optimization of MPS in quantum-inspired numerical analysis

This work discusses the solution of partial differential equations (PDEs) using matrix product states (MPS). The study focuses on the search for the lowest eigenstates of a Hamiltonian equation, for which five algorithms are introduced: imaginary-time evolution, steepest gradient descent, an improved gradient descent, an implicitly restarted Arnoldi method, and density matrix renormalization group (DMRG) optimization. The first four methods are engineered using a framework of limited-precision linear algebra, where operations between MPS and matrix product operators (MPOs) are implemented with finite resources. All methods are benchmarked using the PDE for a quantum harmonic oscillator in up to two dimensions, over a regular grid with up to $2^{28}$ points. Our study reveals that all MPS-based techniques outperform exact diagonalization techniques based on vectors, with respect to memory usage. Imaginary-time algorithms are shown to underperform any type of gradient descent, both in terms of calibration needs and costs. Finally, Arnoldi like methods and DMRG asymptotically outperform all other methods, including exact diagonalization, as problem size increases, with an exponential advantage in memory and time usage.
On the Existence of a Complexity in Fixed Budget Bandit Identification

In fixed budget bandit identification, an algorithm sequentially observes samples from several distributions up to a given final time. It then answers a query about the set of distributions. A good algorithm will have a small probability of error. While that probability decreases exponentially with the final time, the best attainable rate is not known precisely for most identification tasks. We show that if a fixed budget task admits a complexity, defined as a lower bound on the probability of error which is attained by the same algorithm on all bandit problems, then that complexity is determined by the best non-adaptive sampling procedure for that problem. We show that there is no such complexity for several fixed budget identification tasks including Bernoulli best arm identification with two arms: there is no single algorithm that attains everywhere the best possible rate.
Shortcuts to equilibrium with a levitated particle in the underdamped regime

We report on speeding up equilibrium recovery in the previously unexplored general case of the underdamped regime using an optically levitated particle. We accelerate the convergence towards equilibrium by an order of magnitude compared to the natural relaxation time. We then discuss the efficiency of the studied protocols, especially for a multidimensional system. These results pave the way for optimizing realistic nanomachines with application to sensing and developing efficient nano-heat engines.
Algebraic Compression of Free Fermionic Quantum Circuits: Particle Creation, Arbitrary Lattices and Controlled Evolution

Recently we developed a local and constructive algorithm based on Lie algebraic methods for compressing Trotterized evolution under Hamiltonians that can be mapped to free fermions. The compression algorithm yields a circuit which scales linearly in the number of qubits, is fixed depth for for arbitrarily long evolution times and is applicable to time dependent Hamiltonians, although is limited to simple nearest-neighbor spin interactions and fermionic hopping. In this work, we extend the algorithm to compress circuits simulating evolution with long-range spin interactions and fermionic hopping, thereby enabling embedding of arbitrary lattices onto a chain of qubits. Moreover, we show that controlled time evolution, as well as fermion creation and annihilation operators can also be compressed. We demonstrate our results by adiabatically preparing the ground state for a half-filled fermionic chain, and simulating a $4 \times 4$ tight binding model on ibmq_washington. With these new developments, our results enable the simulation of a wider range of models of interest and the efficient compression of subcircuits.
Genomic Data Sharing under Dependent Local Differential Privacy

Privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce ($\epsilon , T$)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Considering the interdependent privacy risks while sharing genomic data, we also analyze the information gain of an attacker about genomes of a donor's family members by observing perturbed data of the genome donor and we propose a mechanism to select the privacy budget (i.e., $\epsilon$ parameter of LDP) of the donor by also considering privacy preferences of her family members. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).
Project Buddy: Find your Project Partner

Abstract: The quest for that perfect partner from another college/state/country is becoming ever more important in today's world. As calls for proposals are launched every day by various students and working professionals, ideas are being hatched all over the world. It may be instinctive to look around a familiar circle or rely on contacts or networks close to hand to find what is needed to bring a project idea to life. But what if this does not yield the desired result? Here Project Buddy comes into the picture. It is a tool that allows students and working professionals with a project idea to register online on a dedicated section of the website. Once registered to a specific theme or tech stack, project ideas can be posted and a request for ideal partners placed. Best of all, it's completely free to sign up for an account and get started.
Contributions of Digital Competencies toward the Speaking Skills in English Language Classes in Ecuadorian Elementary Schools

Digital competences are key factors for the professional developing of teachers of English as a Foreign Language (EFL) in Ecuador. This work aims to identify the contributions of digital competences in the speaking skills of a group of elementary school children. In the research process, the sample was integrated for nine elementary school students and fifteen EFL teachers took. The methodology selected was a mixed. The research techniques used to collect data were semi-structured interviews, contextual observation, and English language pre-tests and post-tests. Results show the teacher's digital competencies evolution and its contribution to the children speaking skills. It concludes that teachers with a higher level of digital competencies can improve children speaking skills. Thus, the students speaking skills in this study changed from 5,02 overage in the pre-test to 8,64 points in the post-test. The changes in speaking skills are significatively positive.
Identity Verification of COVID-19 Vaccination Status Using Facial Recognition Technology

The face is one of the simplest approaches to differentiate the man or woman identification of every other.Face recognition is a personal identification with COVID vaccinated system that uses personal characteristics of a person to identify the fully vaccinated, partial vaccinated, not vaccinated.Human face recognition procedure basically consists of fully vaccinated, partial vaccinated, not vaccinated, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance with testing and training face images, the next is the introduction, which recognize a face as individuals with fully vaccinated, partial vaccinated, not vaccinated.Stage is then replicated and advanced as a version for face detection (face recognition) is one of the much-studied biometrics technologies and developed by experts.There are fully vaccinated, partial vaccinated, not vaccinated that are currently popular in developed face recognition pattern namely Eigen face method and Fisher face method.Facial image recognition Eigen face method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features [1].The main purpose of the use of PCA on face recognition using Eigen a face was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face fully vaccinated, partial vaccinated, not vaccinated image.
Preferred Language Learning Strategies for Speaking Skills among Female Secondary Students

Speaking skills play a vital role in communication as it is a tool for acquiring English language elements, including other language skills.However, language learning strategies are crucial for students' speaking skills, especially the young ones.Gender has proven to have a significant influence on the language learning approaches utilized.Female students use more strategies than male students; they also use different strategies and have distinct language learning preferences.Hence, this study aims to identify Language Learning Strategies employed by female students of a convent school in Selangor to improve their speaking skills and to identify their preferred language strategies for speaking skills.This study used a questionnaire, and data were analysed using frequency and percentages.The findings showed that female students preferred cognitive and social strategies to enhance their speaking skills.This study implies that further research on the preferred learning strategies should also include the teacher's perception of the efficient learning strategies that significantly impact the student's learning.
Model To Detect Breast Cancer Based On Patient Symptoms

The number of medical data warehouses is expanding quickly these days. As a result, it is difficult for us to predict or analyse these facts in order to uncover hidden knowledge that is valuable. For forecasting medical analysis, many machine learning methods and tools are employed. The most prevalent and well-known malignancy, particularly among women, is breast cancer. It ranks among the leading global causes of death. The sole remedy is early detection, which lowers the mortality rate from breast cancer. Breast cells can develop into cancer, which is known as breast cancer. Breast cancer has recently become a highly serious disease, not just in India but also in other nations. The primary goal of this research is to diagnose breast cancer patients as early as possible. Three machine learning approaches Decision Tree, Support Vector Machine, and Logistic Regression are employed for the early detection and prevention of breast cancer patients. These techniques help reduce waiting times and human and technical errors in breast cancer diagnosis. By employing these methods, we can increase the number of lives saved and decrease the death rate by maximising early diagnosis of breast cancer. The likelihood that an infection will be successfully treated depends on precisely identifying and locating it as soon as possible using logistic regression and SVM. A significant obstacle to the diagnosis of breast cancer is the classification of the appropriate machine learning technique. Thus, in order to analyse risk levels that contribute to prognosis, we developed a model for a breast cancer early prediction system. Doctors can diagnose breast cancer using this experimental study, and patients can benefit from early therapy to prolong their lives.
Comparative Performance Evaluation Results of Classification Algorithm in Data Mining to Identify Types of Glass Based on Refractive Index and It’s Elements

Data science is becoming familiar to the public and companies in the era of Industrial Revolution 4.0. One part of data science is data mining. Data mining is the process of collecting information to see patterns from very large datasets and data discovery which is processed in such a way as to become knowledge based on the interpretation of the information obtained. The purpose of this paper is to compare the performance evaluation results of several classification algorithms in data mining (such as DT C-45, Neural Network, KNN, LDA, Naïve Bayes, SVM, and Rule Induction) for identifying types of glass based on the refractive index and its elements. The data set used is a glass identification dataset taken from the UCI Machine Learning Repository. The results of the evaluation can be seen from the criteria like of Accuracy and Kappa using 10 fold cross validation. As a result, the K-Nearest Neighbors (KNN) algorithm has the best Accuracy and Kappa values, namely 72.90% for Accuracy and 0.632 for Kappa values. To determine the significance of the accuracy value, the T-Test method is used.
Utilizing Data Analytics to Analyze Online Purchase Behavior

The emergence of data analytics has fundamentally transformed supply chain management strategies in the global marketplace during the past decade. Classification is one of the most popular methods and receives a great deal of attention in the literature, but there are still some questions concerning the performance characteristics of different classification methods. This paper analyzes three different classification methods: classification trees, k-nearest neighbors, and artificial neural networks to determine if there are any performance gaps between the methods. A series of experiments are conducted utilizing the Analytic Solver Data Mining (formerly XLMiner) add-in to Microsoft Excel in an effort to address these issues. The analysis reveals that there may be minor performance gaps, but the methods all perform well in the context of this study.
STUDYING THE PROBLEM OF DEVELOPING PROFESSIONAL COMMUNICATIVE COMPETENCES OF FUTURE OFFICERS

In the article, the problem of developing professional and communicative competences of future officers - the forms, methods and conditions of its development is studied as a subject of experimental psychological and pedagogical research.
Trifocalizing Instruction, Research, and Extension Functions in Outcome-Based Graduate Education Programs at Romblon State University in the "New Normal”

This paper aims to map the curriculum of the graduate courses in Education offered by Romblon State University, in the Philippines, in the “new normal” ushered in by the Covid-19 pandemic. To do so, it combines an assessment of student feedback and performance with an in-depth documentary analysis of existing policies, past projects, and activities of the university’s Graduate School. Having made this analysis, the paper presents a number of recommendations, namely that 1) the curriculum is re-visited to ensure alignment between the university’s mission and vision, on the one hand, and the learning outcomes, structure, and proposed learning outcomes of the graduate programs in Education on the other, and 2) the curriculum mapping presented in this paper is applied and implemented with a view to achieving consonance between the university’s vision and mission – which sets the outcomes and practice – and which is operationalized in this paper as the class practices and activities to be adopted in Romblon State University’s Master’s and Doctoral programs in Education.
The reality of applying the system of financial incentives in the development of administrative creativity among employees of Kumaran Company for Industry and Investment in Yemen

This study examines the impact of incentives on the development of administrative creativity among the employees of Kumaran Company for Industry and Investment in Yemen. The study sample consists of 186 public administration employees. The data was collected through a questionnaire, and 168 responses were analyzed using analytical descriptive methods. The study found that the company provides workers with financial incentives, with an average score of 4.16 and a high degree of agreement from the sample. The study also found a high level of agreement with the availability of administrative creative dimensions in the company, with an average score of 4.59. The study did not find any significant differences in responses based on demographic variables such as age, scientific qualification, job title, or years of service.
The Dark Web: Privacy and Anonymity

Abstract: The Internet as a whole is a network of multiple computer networks and their vast infrastructure. The network consists of websites accessible through search engines such as Google, Firefox, etc. It's called the Surface Web. The Internet is further subdivided into the Deep Web – content that is unindexed and inaccessible to traditional search engines. The Dark Web is considered part of the Deep Web. It is accessible via TOR. Participants on the dark web are anonymous and hidden. Anonymity, confidentiality and the possibility of not being detected are three factors offered by special browsers like TOR and I2P. In this article, we discuss the impact of the Darknet in different social areas and provide results. It gives the number of daily anonymous darknet users (with TOR) in Kosovo and worldwide at the time showed the impact of hidden service sites and got search engine results from Ahimia and Onion City Dark Web. Anonymity on the dark web is not sufficiently verified. TOR strives to provide anonymous activity and aims to provide anonymous activity. Here are the results for showing the number of users and their location. calculations are based on IP addresses by country code, and accesses the sources of those addresses and reports the numbers in aggregate form. In this way,dark web users are indirectly represented. Another key factor is the number of anonymous network users on the dark web. In such a network, users are counted based on requests from directory clients (via TOR metrics) and the relay list is updated. Indirectly, the number of users calculated for the anonymous networks.
Secure Identification at Your Fingertips: Building a Face Recognition System with Google Colab

Abstract: The term "security" refers to the measures taken to ensure protection against harm. A security system is the result of measures taken to secure something through a system. There are various types of security systems available today, and ongoing research is being conducted in this field. The purpose of this research paper is to develop a face recognition system to enhance security, with the aim of making it more reliable and secure than existing systems. This will be achieved through the use of Google Colaboratory (Colab) for face recognition, which will enable the system to accurately identify individuals based on their facial features. The system will automatically distinguish between real and fake samples to prevent unauthorized access attempts. If a fake access attempt is detected, the system will display a message indicating that the user is unauthorized. The proposed method aims to improve accuracy, efficiency, clarity, and security, ultimately enhancing the overall security of the system.
Advanced Training of the Teaching Staff of the University in a Remote Format Using Information and Computer Technologies

The purpose of the article is to highlight the features of the remote format of professional development of the teaching staff using information and computer technologies and the possibility of applying acquired competencies in the development of the teacher’s personality. The tasks include an empirical study on a representative sample of teachers from four universities in Rostov-on-Don, who have undergone at least once advanced training remotely in order to critically assess the effectiveness and expediency of this process in the real practice of the teacher. The results of theoretical and practical study of the issue are structured in tables, which summarize the main conclusions about the advantages and disadvantages of the distance format of advanced training of a university teacher and the commensurability of this in the formation of his personal growth. The conclusions of the study were discussed collectively in order to make a decision on the professional development of the teaching staff as a basis for the personal growth of the teacher, corresponding not only to the subjective ambitions of the university, but also to the real needs of the labor market and the economy. The study did not show positive results of the perception of advanced training in a remote format using information and computer technologies, but allowed us to direct the vector towards consolidating efforts in improving the qualifications of the teaching staff, which is reflected in the algorithm for expanding the capabilities of the remote format and increasing its effectiveness.