Reviewerly/exHarmony huggingface dataset:
Born Again Neural Rankers.

We introduce Born Again neural Rankers (BAR) in the Learning to Rank (LTR) setting, where student rankers, trained in the Knowledge Distillation (KD) framework, are parameterized identically to their teachers. Unlike the existing ranking distillation work which pursues a good trade-off between performance and efficiency, BAR adapts the idea of Born Again Networks (BAN) to ranking problems and significantly improves ranking performance of students over the teacher rankers without increasing model capacity. The key differences between BAR and common distillation techniques for classification are: (1) an appropriate teacher score transformation function, and (2) a novel listwise distillation framework. Both techniques are specifically designed for ranking problems and are rarely studied in the knowledge distillation literature. Using the state-of-the-art neural ranking structure, BAR is able to push the limits of neural rankers above a recent rigorous benchmark study and significantly outperforms traditionally strong gradient boosted decision tree based models on 7 out of 9 key metrics, the first time in the literature. In addition to the strong empirical results, we give theoretical explanations on why listwise distillation is effective for neural rankers.
GAN-Control: Explicitly Controllable GANs

We present a framework for training GANs with explicit control over generated images. We are able to control the generated image by settings exact attributes such as age, pose, expression, etc. Most approaches for editing GAN-generated images achieve partial control by leveraging the latent space disentanglement properties, obtained implicitly after standard GAN training. Such methods are able to change the relative intensity of certain attributes, but not explicitly set their values. Recently proposed methods, designed for explicit control over human faces, harness morphable 3D face models to allow fine-grained control capabilities in GANs. Unlike these methods, our control is not constrained to morphable 3D face model parameters and is extendable beyond the domain of human faces. Using contrastive learning, we obtain GANs with an explicitly disentangled latent space. This disentanglement is utilized to train control-encoders mapping human-interpretable inputs to suitable latent vectors, thus allowing explicit control. In the domain of human faces we demonstrate control over identity, age, pose, expression, hair color and illumination. We also demonstrate control capabilities of our framework in the domains of painted portraits and dog image generation. We demonstrate that our approach achieves state-of-the-art performance both qualitatively and quantitatively.
Association Rule Mining And Data Sanitization Technique Using Hash Base Apriori

The private information is compassionate under different circumstances and needs to be sanitized before it is released to resolve privacy concerns. With limited time, data mining techniques can obtain vast quantities of information. Most sensitive material corresponds to an individual or an organization, the information obtained by the sophisticated machine learning algorithm could disclose. Data belonging to an entity or an agency can have different degrees of sensitivity. Such data is only made accessible to approved users or entities. Therefore, confirming the security of complex data by access constraints is not a complete operation. It can affect the usefulness of the product of data mining, and the user can re-identify sensitive data.   To find a mechanism for the security of confidential information by introducing tools and techniques for data mining that could be applied to databases, despite it is compromising the reliability of the outcome of data mining. In this article, we proposed data sanitization technique using frequent itemset classification approach with modified apriori algorithm. The difficulty is on maintaining actionable intelligence for necessary arrangements, but at the corresponding period not suffering the numerous exhibition of corporation rule mining. Thorough analysis of various sequential pattern algorithms for achieving the privacy of extensive data using data sanitization technique. Our investigations illustrate that our algorithm is efficient, scalable, and performs meaningful correction over the other methods conferred in the existing systems.
Public Sentiments Analysis through Tweets on the COVID-19 Pandemic: A Comparative Study and Performance Assessment

In these modern computing days, the realm of sentiments analysis (SA) and opinion mining (OM) has captured enough attention due to the availability of massive though unstructured data available over miscellaneous social media platforms, blogs, e-commerce portals, and other similar digital resources. The outbreak of the COVID-19 pandemic in 2020 led to such excessive public discussions over various social media platforms, and people exhibited their emotions of fear, threat, hope, faith etc. in their posts. Twitter has been a very popular social media platform among the general public, celebrities, government personnel, entrepreneurs, etc. In the present study, people’s emotions and opinions about the current pandemic, expressed through Tweets, were extracted, interpreted, and analyzed through various machine learning (ML) techniques. We hypothesize a three-tier model for the whole research process. Being unstructured, uncertainty and imprecision are inherent in this data, and this needs to be normalized before extracting the vital information. Therefore, the Tweets dataset is passed through various data preprocessing treatments first for repairing and removing dirty data. The refined Twitter data are then visualized in a better way to seek interesting insights and trends. Next, five popular ML classifiers are employed for sentiments classification and comparative performance assessment through the Google Colab platform. Tweets are classified into negative, neutral, and positive genres based on polarity scores, and the ML classifiers are compared over the standard performance parameters like accuracy, precision, recall, and F-measure.
Predicting Ethnicity from Names with rethnicity: Methodology and Application.

In this study, a new R package, \texttt{rethnicity} is provided for predicting ethnicity based on names. The Bidirectional LSTM and Florida Voter Registration were used as the model and training data, respectively. Special care was given for the accuracy of minority groups, by adjusting the imbalance in the dataset. The models were trained and exported to C++ and then integrated with R using Rcpp. Additionally, the availability, accuracy, and performance of the package were compared with other solutions.
Penyusunan Bahan Ajar Mata Pelajaran Finance Accounting Pada SMK Batam Business School

Teaching materials play an important role for teachers in learning process with students in schools, one of which is the Batam Business School Vocational High School. The lack of modification of teaching materials and less updated reference sources in Financial Accounting class XI odd semester is a problem that is being faced by Batam Business School Vocational High School in the midst of COVID-19 pandemic in using online learning models. This learning model utilizes available technology and applications so that the world of education can continue. The methods used to solve this problem are the interview method and the documentation method. The output of this activity, there are ten files of powerpoint and three learning videos that can be accessed on Youtube that related to material on credit sales transactions, installment sales, and drafts, accounts receivable, the method of eliminating uncollectible accounts receivable, notes receivable, accounts receivable card, accounts payable short term, accounts payable card, inventory, inventory valuation methods, and cost of goods manufactured. The results achieved from this activity are the Financial Accounting subject teacher at SMK Batam Business School felt helped in the preparation of odd semester teaching materials that would be used in the learning process with XI grade students and in accordance with the basic competencies applied in schools.
An Approach for OCR detection and Classification for Devanagari Printed Text using Deep Learning

Optical Character Recognition (OCR) is the method of interpreting text from digital documents automatically. It is a large area of signal processing science. In several languages of India, such as Hindi, Nepali, Marathi, Sindhi etc., Marathi has used. The Marathi language is used by more than 300 million people worldwide. This pattern forms the basis of the communities of India. It plays a significant role in the production of manuscripts and literature. OCR research has been done for in banks, post offices, defence organizations, library modernization, etc., because of its potential applications. Several techniques are available for the character segmentation of handprint Gujrati, Bangla, Tamil, Hindi, etc., with these methodologies. However, much work is done for both the given material, but for the laboratories, it is only limited.. In this paper, proposed a Deep Learning based Convolutional Neural Network (CNN) classifier technique has used for OCR System of printed as well as scanned newsprint Marathi script. This research system deals with various feature extraction and feature selection techniques with CNN classification. In experimental analysis we train system around more than 51 characters that produces better detection accuracy for test images
Quantum metrology and quantum correlations

Abstract Given a physical system to define a suitable measurement process on it constitutes one of the most crucial and challenging issues in physics. According to the particular theory under consideration, taking measurements has more or fewer subtleties but one ubiquitous concept for every physical theory is the uncertainty. In quantum mechanics, it is possible to identify a fundamental unpredictability given by Heisenberg uncertainty relations and the indistinguishability of quantum states, together with a practical uncertainty related to unavoidable errors in the measurement process. Considering only these fundamental ties, the estimation of unknown values of physical magnitudes has well-defined precision limits. The Cramer–Rao bound is a cornerstone for the analysis of these restrictions and an irreplaceable tool to determine the most accurate measurement procedures. On the quantum side, there exist quantum correlations known as entanglement and quantum discord which bring new ways to overcome the classical precision limits. Quantum metrology is a relatively young emerging area whose main aim is to study how to improve parameter estimation theory by using quantum correlations present in multipartite systems.
The Adoption of Edmodo in a Hybrid EFL Writing Class: What do Indonesian Students and Lecturers Say?

Hybrid learning integrates conventional teaching and learning with educational technologies and combines the advantages of on-site class and online learning. Edmodo, as a cloud-based informative Social Networking Site (SNS) has much been used in entirely virtual as well as hybrid learning all over the world. This paper presents a discussion of lecturers' and students’ perspectives of the adoption of Edmodo as a learning platform in some hybrid English as a foreign language (EFL) writing classes, with a particular focus on its benefits. With two lecturers and 65 students as the participants, the collection of data in this study was through observation, questionnaire, and semi-structured interviews. Generally, the results indicated that the lecturers and the students shared positive experiences towards the adoption of Edmodo as a supplement to the on-site writing instruction. Specifically, the participants mentioned that “user-friendly features,” “opportunity to collaborative learning,” and “enhancement of students’ participation” are among its benefits. This study implies that understanding users’ perceptions of the learning platforms used in a hybrid class may maximize the impact of technology integration into education.
Towards Accountability in CRS Generation.

It is well known that several cryptographic primitives cannot be achieved without a common reference string (CRS). Those include, for instance, non-interactive zero-knowledge for NP, or maliciously secure computation in fewer than four rounds. The security of those primitives heavily relies upon on the assumption that the trusted authority, who generates the CRS, does not misuse the randomness used in the CRS generation. However, we argue that there is no such thing as an unconditionally trusted authority and every authority must be held accountable for any trust to be well-founded. Indeed, a malicious authority can, for instance, recover private inputs of honest parties given transcripts of the protocols executed with respect to the CRS it has generated.
Human Subjects Data Essentials Data Curation Primer

Alfred P Sloan Foundation and the Institute of Museum and Library Services RE-85-18-0040-18
Maximizing Extensive Listening For Assisting Students' Speaking Ability Ability(Qualitative Research at the Eleventh Grade Students ofMAN 1 Lebak Academic year 2020/2021)

Taufik Hidayat Srn. 1612301212020. Maximizing Extensive Listening for Assisting Students’ Speaking Ability. (Qualitative Research at Eleventh Grade Student of MAN 1 Lebak).Speaking is one of the basic language skills that has important role to be mastered by students in order to be a good communicator. But, many students in senior high school of MAN 1 Lebak face some problems dealing with speaking English. The researcher applied extensive listening to assist students’ speaking ability. Because extensive listening is one type of media that can improve students’ speaking ability as stated by Matsuo. Then, can be interesting media for a learning speaking. The aims of this research to analyze how is maximizing extensive listening to assist students’ speaking ability. The objective of this research is to find out how far maximizing extensive listening can assist students’ speaking ability. This research used qualitative method, the instrument used in this research was tests (before using extensive listening and after using extensive listening) and interview. Then, to analyze the data the researcher used the theory from Lodico, et al. The participants of this research was three students of eleventh grade student of MAN 1 Lebak Academic year 2020/2021. The test result of this research showed that the speaking ability of each students’ after using extensive listening was higher than before using extensive listening. There were improvements from the indicators of speaking ability because of this research, the five indicators of speaking ability are grammar, vocabulary, comprehension, fluency, and pronunciation. Furthermore, the result of the interview showed that all of the students’ agree that learning speaking by using extensive listening is effective enough and makes interesting for learning speaking. Based on the result above, it can be conclude that using extensive listening could be assist students’ speaking ability
Efficient Verification of N-GHZ State in Quantum Wireless Network Communication

Quantum communication has an irreplaceable role in the confidentiality of information compared with classical communication. The entangled state was used in the quantum communication networks to transmit information, and quantum wireless multi-hop networks communication can be realized by using the quantum entanglement swapping, thereby ensuring that information can be transmitted over long distances. Based on the security problems caused by the direct use of entangled states in quantum communication, this paper verifies the GHZ state in the quantum wireless network communication, so as to ensure the GHZ state is the perfect entangled state originally prepared, thus ensuring the safety of quantum communication.
Peningkatan Aktivitas Dan Hasil Belajar Siswa Melalui Pendekatan Inkuiri Pada Pembelajaran IPA Kelas V Sekolah Dasar

At the learning of students' activities on science learners is low and tend to use lecture methods. The lack and lack of student activity in the study of science can affect the students' learning. The purpose of this study is to find out the positive correlation between student activity and the results of studying earth class IV SDN 07 TUIK 2017/2018. Research is done in 2 cycles and each cycle consists of two meetings. The study instumen consists of an objective text, an essay, and an observation sheet of student activity and the teacher aspect. Research shows an increase in students' activities of the 1 meeting of 1 59.31% and the second meeting was increased by 68.13%. The second cycle of meeting I was 85,78% and the second meeting had an increase of 90,68%. The sharp results of each student study cycle I 69.97%, and on cycle ii is 80.18%. Based on analysis and discussion of research results it suggests that  the  positive  correlation  between  learning  activities  and  the  study  of  science students iv SDN 07 Tuik year 2017/2018.
A Fundamental Study of Artificial Intelligence and Its Legal Implications in India

: The ability of machines to execute cognitive tasks like thinking, perceiving, learning, problem solving and decision making, is termed as Artificial Intelligence (AI). AI has evolved in ways that far exceed its original conception, and initially it is conceived as a technology that could mine human intelligence. With the help of incredible advances that are made in data collection, processing and computation power, intelligent systems can now be able to deploy to perform a variety of tasks, enable connectivity and improve efficiency. As we have its utility in a growing number of fields due to dramatic increment in AI’s capability. India future lies in sixth of the world’s population. Artificial Intelligence (AI) role is shaping in India’s development and growth is bound to be substantial as its revolution sweeps through societies and enters in our daily life. Despite of the fact that AI provide mechanisms to leapfrog traditional hurdles such as poor infrastructure and bureaucracy but for India, AI holds promise as catalyst to accelerate progress. In emerging technologies, superior research capabilities have been the cornerstone of leadership aspirations and effectively realizing the growth potential which requires expertise in both core and applied research. Despite indications of recent positive efforts in this aspect of technology, in India AI research is still considered infancy and need large scale concerted and combined interventions. In this paper, with various Indian perspectives of AI we also describe fundamentals of Artificial intelligence, various opportunities and challenges for AI in India and also the work provides the brief scenario about the legal perspective of the AI in India.
Impact of the Entrepreneurial Leadership on Knowledge Management Effectiveness: The Role of Organizational Trust

Background: Many organizations are facing challenges of implementing knowledge management practices into their business model. As knowledge management is paving its way into strategic focus of organizations that’s why this study focused towards the role of Entrepreneurial leadership in enhancing effective knowledge management practices in the organization.Purpose of the Study: The purpose of this study was to examine whether any significant role of entrepreneurial leadership towards the achievement of knowledge management effectiveness while exploring the moderating role of organizational trust in the perspective of fabric manufacturing industries of selected cities (Lahore, Rawalpindi and Islamabad) of Pakistan and it’s theoretical framework is supported by the social learning theory.Methodology/Approaches: Data were collected from 100 entrepreneurial leaders and 260 samples were their employee’s through convenient sampling technique. Measuring scale was adopted and responses got on 5-point Likert Scale. Confirmatory Factor Analysis, Regression Analysis and SEM to estimate the structural model fit data adopting Hair and Anderson’s model.Finding: The present study finds out that the entrepreneurial leadership have a significant role to enhance the knowledge management initiative in the organization and also find out that organizational trust have great impact as a moderator between the entrepreneurial leadership and successful implementation of knowledge management practices in the organization.Value/Implications: This paper provided a new insights for research scholars, practitioners and also Govt. policy making department that how entrepreneurial leaders play a critical role in the advancement of the country. Limitations of this study and future research direction also discussed in the end.
Adversarial Examples by Perturbing High-level Features in Intermediate Decoder Layers

We propose a novel method for creating adversarial examples. Instead of perturbing pixels, we use an encoder-decoder representation of the input image and perturb intermediate layers in the decoder. This changes the high-level features provided by the generative model. Therefore, our perturbation possesses semantic meaning, such as a longer beak or green tints. We formulate this task as an optimization problem by minimizing the Wasserstein distance between the adversarial and initial images under a misclassification constraint. We employ the projected gradient method with a simple inexact projection. Due to the projection, all iterations are feasible, and our method always generates adversarial images. We perform numerical experiments on the MNIST and ImageNet datasets in both targeted and untargeted settings. We demonstrate that our adversarial images are much less vulnerable to steganographic defence techniques than pixel-based attacks. Moreover, we show that our method modifies key features such as edges and that defence techniques based on adversarial training are vulnerable to our attacks.
Digital Orientalism: #SaveSheikhJarrah and Arabic Content Moderation

In this paper, we zero in on Arabic content moderation. We identify the systemic policies that are being administered by social media companies, whether designed within the technology or implemented through policies. While there have been issues with other platforms, we focus our analysis and argument in this paper on Facebook as the most egregious violator, with far reaching systemic problems and impact on Arab and pro-Palestine content. Second, we identify the various formats of digital repression of speech regarding the rights movement supporting Palestinians online. Third, we look at the countermeasures users have employed to overcome this repression as part of a greater movement for accountability from platforms in the Arab region. In this paper, we contend that failures and subjectivity of platform governance has given rise to what we call a new orientalism in the digital sphere, or digital orientalism. Orientalism is the stereotypical and discriminatory lens by which western nations view the Middle East and North African region. Western countries have used this lens to assert dominance and colonialism, either through war, media, governance and policies. We argue that this framework now defines the policies and actions Western social media companies use to disadvantage Internet users in this region.