Reviewerly/exHarmony huggingface dataset:
Man versus Machine: AutoML and Human Experts' Role in Phishing Detection.

Machine learning (ML) has developed rapidly in the past few years and has successfully been utilized for a broad range of tasks, including phishing detection. However, building an effective ML-based detection system is not a trivial task, and requires data scientists with knowledge of the relevant domain. Automated Machine Learning (AutoML) frameworks have received a lot of attention in recent years, enabling non-ML experts in building a machine learning model. This brings to an intriguing question of whether AutoML can outperform the results achieved by human data scientists. Our paper compares the performances of six well-known, state-of-the-art AutoML frameworks on ten different phishing datasets to see whether AutoML-based models can outperform manually crafted machine learning models. Our results indicate that AutoML-based models are able to outperform manually developed machine learning models in complex classification tasks, specifically in datasets where the features are not quite discriminative, and datasets with overlapping classes or relatively high degrees of non-linearity. Challenges also remain in building a real-world phishing detection system using AutoML frameworks due to the current support only on supervised classification problems, leading to the need for labeled data, and the inability to update the AutoML-based models incrementally. This indicates that experts with knowledge in the domain of phishing and cybersecurity are still essential in the loop of the phishing detection pipeline.
A Robust and Simplified Method of Machine Learning To Predict the Loan Approval Process

Decision model for investment supplier and theorist is set up, and accordingly the impact of utilization support on resource allotment and the impact on resource distribution of funding supplier is contemplated. The examination shows that, and funding organization would build the heap of speculation to investment project because of utilization support.
Library Guides: REDCap at Berkeley: Research Resources

This guide has information about REDCap at Berkeley including campus availability, training videos, and how to get help.
Sentence-Level Document Novelty Detection Using Latent Dirichlet Allocation with Auto-Encoders

Novelty detection is a classical one-class classification problem in machine learning that attempts to categorize the ‘known’ data inputs from the ‘unknown’ inputs mainly using unsupervised learning approaches. There are many approaches available for detecting the novelty from the text documents, primarily on the sentence level, with varying degrees of success based on the methodology they adopt. The advancements in deep learning have contributed significantly for developing more effective algorithms for detecting the novelty. This work primarily focuses on finding sentence-level document novelty using latent Dirichlet allocation (LDA), which is one of the globally accepted and extensively used topic modeling algorithms by combining it with auto-encoders. Experiments on benchmarked datasets indicate that the proposed approach outperforms some of the state-of-the-art approaches in novelty detection.
일부 대학생의 코로나19에 대한 건강예방행위에 미치는 영향 : 건강신념모델을 중심으로

Purpose: The purpose of this descriptive research is to identify how stress from Covid-19, health beliefs, and social support of college students affect health prevention. Methods: The subjects of the study were 128 university students, excluding health major students, at one university in D City. The survey was conducted from August 1 to 31, 2020. The survey questionnaire consists of 8 items on stress from COVID-19, 12 items adapted from a health belief measurement tool, 12 items from a social support measurement tool, and 11 items adapted from a tool that measures health preventive behaviors. The collected data were analyzed using the hierarchical multiple regression analysis method with SPSS 26.0. Results: In model 1, stress from COVID-19 was statistically significant (β=-.403, p=.003). Model 2 added four health belief factors into Model 1. Stress (β=-.419, p<.001), perceived severity (β= -.193, p=.030), and perceived barriers (β=-.182, p=.009) were statistically significant. In model 3, stress (β=-.413, p<.001), perceived barriers (β=-.147, p=.034), and social support (β=.194, p=.011) were statistically significant. The regression equation was significant (F=15.395, p=<.001) and the model's explanatory power was 53.1%. Conclusion: The results show that when college students had a high degree of health beliefs about COVID-19, the degree of health preventive behaviors was proportionally high. To make them practice preventive health behaviors, it is necessary to develop infection control education programs to improve health beliefs.
Implementasi Metode Moving Average Sebagai Prediksi Penjualan Perlengkapan Pertanian Pada CV. Aneka Tani

CV. Aneka Tani is a shop that sells various agricultural equipment. The problems in CV. Aneka Tani are the process of recording transactions that are still done manually with a sales book and there is no sales prediction system. It causes waste of paper, human error in transactions, and bad stock management. The purpose of this research is to predict sales of agricultural equipment, applying the Moving Average algorithm to CV. Aneka Tani’s data to generate sales prediction models, and analyze predictive models. The research method used is the CRISP-DM (Cross Industry Standard Process for Data Mining) which consists of business understanding, data understanding, data preparation, modeling, evaluation, and implementation. The process of collecting data is done by conducting interviews with business owners. There were 6 products used in Moving Average method. The stable product is Dafat with MAD value is 0,9 and MSE value is 1,2. The non stabel product is Phonska with MAD value is 13,6 and MSE value is 245,7.
Between Yarns and Electrons : A method for designing textural expressions in electromagnetic smart textiles

The design of smart textiles presents a discrepancyof scale where the designer works at the level ofstructural textile design while facets of the materialexpress at scales beyond one’s senses. With ...
Representing contextual associations in convolutional neural networks

Contextual associations play a significant role in facilitating object recognition in human vision. However, the role of contextual information in artificial vision remains elusive. We aim to examine whether contextual associations are represented in an artificial neural network, and if so, to understand at what layer they potentially have a role. Addressing this, we examined whether objects that share contextual associations (e.g., bicycle-helmet) are represented more similarly in convolutional neural networks than objects that do not share the same context (e.g., bicycle-fork), and further examined where in the network these context-based representational similarities emerge. As a comparison, we also examined the representational similarity of objects that belong to the same category (e.g., two different shoes) in contrast to objects that do not share the same category (e.g., shoe-brush). In a VGG16 neural network trained on ImageNet and focused on object categorization, representational similarity among objects that share a context (N = 70) is substantially higher than similarity among objects that do not share a context. Representational similarities were computed as the correlation between unit responses to pairs of images in and out of context (or category as a comparison). This context-based rise in similarity emerged at very early layers of the network, remarkably, at the same layer that category-based similarity was found. Category-based similarity was significantly larger than context-based similarity throughout the network. Pixel similarities across contextually paired objects were no greater than objects that do not share the same context. Thus, even though the network was designed for categorical object recognition, contextual relationships were evident in the network across early, mid, and late layers. This suggests that context is inherently preserved and represented across the network, and may have a critical role in facilitating object recognition both in humans and in artificial models.
PRIVACY PROTECTION OF CLOUD DATA USING SMARTCARD AUTHENTICATION

In current days cloud computing is providing great flexibility for the end- users to store and access a lot of valuable information to and from remote servers. As we all know that data is uploaded into the cloud is outsourced to a third party untrusted remote server, privacy for that data is almost a big problem for the enterprises. Hence in this current project, we try to add a new level of security for the cloud data by adding biometric authentication techniques like fingerprint images and then verify the user authentication based on the biometric images. Here we try to design a mutual authentication scheme based on a smartcard for cloud computing to avoid the illegal data access by unauthorized users and in which this will be divided into two phases for providing security. Here we try to use finger print images as main source of security and also try to authenticate them by using same images during upload or download time. By conducting various experiments on our proposed method we finally came to a conclusion that proposed method is best in providing data security in the cloud server.
LibGuides: Recursos de Coronavirus para hispanohablantes en Santa Clara | Coronavirus Resources for Spanish Speakers in Santa Clara: Sitios web útiles | Helpful Websites

The purpose of this guide is to provide resources, such as books, e-books and websites, dedicated for Spanish speakers to easily find information on the novel coronavirus (COVID-19)
A Security Model and Fully Verified Implementation for the IETF QUIC Record Layer.

Drawing on earlier protocol-verification work, we investigate the security of the QUIC record layer, as standardized by the IETF in draft version 30. This version features major differences compared to Google’s original protocol and early IETF drafts. It serves as a useful test case for our verification methodology and toolchain, while also, hopefully, drawing attention to a little studied yet crucially important emerging standard.We model QUIC packet and header encryption, which uses a custom construction for privacy. To capture its goals, we propose a security definition for authenticated encryption with semi-implicit nonces. We show that QUIC uses an instance of a generic construction parameterized by a standard AEAD-secure scheme and a PRF-secure cipher. We formalize and verify the security of this construction in F. The proof uncovers interesting limitations of nonce confidentiality, due to the malleability of short headers and the ability to choose the number of least significant bits included in the packet counter. We propose improvements that simplify the proof and increase robustness against strong attacker models. In addition to the verified security model, we also give a concrete functional specification for the record layer, and prove that it satisfies important functionality properties (such as the correct successful decryption of encrypted packets) after fixing more errors in the draft. We then provide a high-performance implementation of the record layer that we prove to be memory safe, correct with respect to our concrete specification (inheriting its functional correctness properties), and secure with respect to our verified model. To evaluate this component, we develop a provably-safe implementation of the rest of the QUIC protocol. Our record layer achieves nearly 2 GB/s throughput, and our QUIC implementation’s performance is within 21% of an unverified baseline.
Unified and Multilingual Author Profiling for Detecting Haters.

This paper presents a unified user profiling framework to identify hate speech spreaders by processing their tweets regardless of the language. The framework encodes the tweets with sentence transformers and applies an attention mechanism to select important tweets for learning user profiles. Furthermore, the attention layer helps to explain why a user is a hate speech spreader by producing attention weights at both token and post level. Our proposed model outperformed the state-of-the-art multilingual transformer models.
Analysis on the Effectiveness of Bung Hatta University's Promotion

There has been tight and steep competition in Indonesian universities for prospective students due to the increasing growth of private higher education or universities in all regions. Bung Hatta University as one of the largest universities in the Higher Education Service Institutions (LLDIKTI) region X also faces the impact of this competition. This study aims to analyze and measure the effectiveness of promotions that have been carried out by Bung Hatta University in attracting new prospective students. This research is a descriptive study that aims to describe, explain, and validate research findings. This study found that the main source of information for students obtaining information about Bung Hatta University, including all faculties available, was the nuclear family or close family at 37.99%, the second source of information with a proportion of 19.55% was promotions on social media, internet and websites. Information from students currently studying comes in the third place with a proportion of 12.99%. The fourth place is alumni with a proportion of 8.84% , the fifth place is information from friends or neighbors, as much as 7, 82%, the sixth place is from electronic media at 5.17%, the seventh place is from Senior high school teachers in which the students study 2.09%, the eight source is from the visit of the university promotion team is 1.96%, the ninth source is from mass media, 1.96% and the last one, in the tenth place is information from employees or lecturers of Bung Hatta University is 1.54%. Several factors that encourage students to study at Bung Hatta University are the first one is the image of Bung Hatta University as Higher Education with a proportion of 60.20%, secondly is the economic aspect of 16.62%, the third factor is the student activities with a proportion of 14.11% and the fourth place is the physical aspect with the proportion of 9.08%.
Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias

Lexical disambiguation is a major challenge for machine translation systems, especially if some senses of a word are trained less often than others. Identifying patterns of overgeneralization requires evaluation methods that are both reliable and scalable. We propose contrastive conditioning as a reference-free black-box method for detecting disambiguation errors. Specifically, we score the quality of a translation by conditioning on variants of the source that provide contrastive disambiguation cues. After validating our method, we apply it in a case study to perform a targeted evaluation of sequence-level knowledge distillation. By probing word sense disambiguation and translation of gendered occupation names, we show that distillation-trained models tend to overgeneralize more than other models with a comparable BLEU score. Contrastive conditioning thus highlights a side effect of distillation that is not fully captured by standard evaluation metrics. Code and data to reproduce our findings are publicly available.
fairgraph v0.5.1

fairgraph is an experimental Python library for working with metadata in the HBP/EBRAINS Knowledge Graph, with a particular focus on data reuse, although it is also useful in metadata registration/curation. This is v0.5.1 of fairgraph.
Innovative Models of Pedagogical Education at the Classical University

The article is devoted to the role of classical university education in the training of highly qualified, highly competitive teaching staff capable of forming the image of a new school in modern conditions. The achievement of such a complex task is ensured by identifying and implementing the heuristic capabilities of a classical university in the development of pedagogical education. The author associates such opportunities with its fundamentalization, research orientation, advanced character, etc. The purpose of the article is to present the results of a scientific study related to the definition and theoretical justification of possible ways to implement models of teacher education as an optimal “scenario” for training elite teachers in a classical university. Methodology and methods of research. The methodological basis of the research is the philosophical provisions about a person as a subject of his own life activity; the unity and interrelation of processes and phenomena, general and special. The general methodology of the research is represented by a system-synergetic approach that allows us to consider professional and pedagogical training in a classical university as an open developing system in the “school-university-school”continuity relations; a holistic approach as a methodological orientation towards the formation of a holistic personality of a future teacher, as well as the integrity of the system of professional and pedagogical training at a classical university; an individual-personal approach that provides an opportunity to study along an individual educational trajectory. The main research methods were the analysis of scientific literature, theoretical generalization and modeling. The results of the study. As a result of the theoretical and experimental work carried out, it is proved that the quintessence of the heuristic capabilities of the classical university determines the development and implementation of innovative models for the development of pedagogical education. The author gives a description of each model, indicating its features and “contribution” to the training of future teachers in the system of university classical education. In conclusion, it is concluded that the proposed innovative variable models for the development of pedagogical education at a classical university will ensure the success of professional pedagogical training of future teachers, bring it to a fundamentally new level by realizing the valuable potential of a classical university.
PENGARUH AKREDITASI DAN KELOMPOK REFERENSI TERHADAP KEPUTUSAN SISWA MELANJUTKAN KULIAH (STUDI KASUS PADA YAYASAN PENDIDIKAN KELUARGA MEDAN)

This study aims to determine the effect of accreditation and reference group on the decision to continue collage. Population in this study were students of SMA Yayasan Pendidikan Keluarga Medan which amounts to 150 students while the technique of sample sampling by randoms sampling with sampling technique using slovin method. So the sample in this study amounted to 35 people. This type of research is a quantitative research. Quantitative research is a research that aims to determine the degree of relationship and pettern/shape of infuence between two or more variables, which with this research will be built a theory that serves to explain, predict and control a symptom.The result of multiple regression analysis that Y = 4,360 + 0,243
Organizational reputation and its role in organizational indulgence :An Analytical study of a sample of the teachers of the University of Warith Al-Anbiya

The present study is concerned with determining the effect of organizational reputation on employee engagement in the University of Warth Al-Anbiya. The study is conducted on (50) teaching staff at University of Warith Al-Anbiya. A questionnaire has been designed to collect the required data to measure the sub-dimensions of organizational reputation with the sub-dimensions of organizational immersion and to determine the level of impact between the variables. A set of statistical methods has been used to measure the relevant variables (mean, standard deviation, correlation coefficient "Pearson"). The study indicates a strong direct correlation between the components of organizational reputation with its dimensions (social responsibility, organization image, creativity, quality of service, Attracting the talented) and between employee engagement with its dimensions (enthusiasm, dedication, assimilation). The study has reached a set of conclusions. A set of proposals and recommendations was put forward, the most important of which is the assess of the capabilities of workers and working to identify their needs for continuous training and qualification to enable them to meet new work requirements as well as attract talents and improve the reputation of an organization.
Dimming Down LED: An Open-source Threshold Implementation on Light Encryption Device (LED) Block Cipher

Lightweight block ciphers have been widely used in applications such as RFID tags, IoTs, and network sensors. Among them, with comparable parameters, the Light Encryption Device (LED) block cipher achieves the smallest area. However, implementation of encryption algorithms manifest side-channel leakage, therefore, it is crucial to protect their design against side-channel analyses. In this paper, we present a threshold implementation of the LED cipher which has 64-bit data input and 128-bit key. The presented design splits secret information among multiple shares to achieve a higher security level. We demonstrate that our implementation can protect against first-order power side-channel attacks. As a cost, the design area is almost doubled and the maximum operating frequency is degraded by 30%. To make our design verifiable, we have also open-sourced our design online.
Towards Retina-Quality VR Video Streaming: 15ms Could Save You 80% of Your Bandwidth.

Virtual reality systems today cannot yet stream immersive, retina-quality virtual reality video over a network. One of the greatest challenges to this goal is the sheer data rates required to transmit retina-quality video frames at high resolutions and frame rates. Recent work has leveraged the decay of visual acuity in human perception in novel gaze-contingent video compression techniques. In this paper, we show that reducing the motion-to-photon latency of a system itself is a key method for improving the compression ratio of gaze-contingent compression. Our key finding is that a client and streaming server system with sub-15ms latency can achieve 5x better compression than traditional techniques while also using simpler software algorithms than previous work.
Pengendali Air Wudhu Otomatis Menggunakan Sensor Ultrasonik Berbasis Arduino

Before performing prayers, Muslims must first take ablution water. So far, the provision of ablution water from mosques and prayer rooms is almost 100% using a faucet that is turned manually, sometimes it is troublesome and water is often wasted (wasteful). Therefore, the purpose of this research is to build a prototype automatic ablution water controller in the form of developing a system with an Arduino-based ultrasonic sensor. The tool that is built based on the prototype will be able to issue ablution water automatically and stop automatically, depending on the limb that is brought close to the end of the pipe that will discharge the water. This monitoring works by using an ultrasonic sensor, where the object under the sensor becomes the input. The ultrasonic sensor will be connected to the Arduino microcontroller which is useful for running this system, which provides information on the relay to activate the mini water pump and release water. So it can be concluded that this system is able to help control the use of ablution water to be more efficient.