Reviewerly/exHarmony huggingface dataset:
The Capital One Breach

The Capital One breach of 2019 was the largest single cloud security breach that had occurred at its time. In the breach, more than 100 million credit card applications were stolen by an ex-Amazon employee (Paige A. Thompson) who went by the Twitter handle (or alias) of “Erratic.” The credit card application data set that she stole contained 140,000 SSNs and 80,000 bank account numbers. Capital One estimated that the breach could cost over $300 million and was fined $80 million in 2020 by the Office of the Comptroller of the Currency, a banking regulator.
Uncertainty quantification and exploration-exploitation trade-off in humans

The main objective of this paper is to outline a theoretical framework to analyse how humans' decision-making strategies under uncertainty manage the trade-off between information gathering (exploration) and reward seeking (exploitation). A key observation, motivating this line of research, is the awareness that human learners are amazingly fast and effective at adapting to unfamiliar environments and incorporating upcoming knowledge: this is an intriguing behaviour for cognitive sciences as well as an important challenge for Machine Learning. The target problem considered is active learning in a black-box optimization task and more specifically how the exploration/exploitation dilemma can be modelled within Gaussian Process based Bayesian Optimization framework, which is in turn based on uncertainty quantification. The main contribution is to analyse humans' decisions with respect to Pareto where the two objectives are improvement expected and uncertainty quantification. According to this Pareto model, if a decision set contains a Pareto efficient (dominant) strategy, a rational decision maker should always select the dominant strategy over its dominated alternatives. The distance from the Pareto frontier determines whether a choice is (Pareto) rational (i.e., lays on the frontier) or is associated to exasperate exploration. However, since the uncertainty is one of the two objectives defining the Pareto frontier, we have investigated three different uncertainty quantification measures and selected the one resulting more compliant with the Pareto model proposed. The key result is an analytical framework to characterize how deviations from rationality depend on uncertainty quantifications and the evolution of the reward seeking process.
The big nudge: here’s how the government could spread its coronavirus tracing app far, fast and wide

The government’s tracing app, COVIDSafe, is off to a flying start, with over 1 million downloads within hours of launch. But what if the rate of uptake slows to the extent that the government’s 40% adoption rate looks unlikely to be achieved. When then?What about giving people incentives to download? And can people be “nudged” along to adopt?
Information and source credibility towards information adoption amongst upper secondary school students

The purpose of this study was to determine the extent to which the information quality and source credibility affect the use and the process of information adoption, especially among upper secondary school students as Twitter users who are prone to hoaxes and fake news. Research subject and method: The stratified sample was 100 upper secondary school students in Surabaya who used Twitter and had read or followed threads. The respondents were asked questions to test their dependence on Twitter and their social media behaviour that affected the information adoption process. Information quality has a significant effect on usability, in contrast to source credibility which does not affect the usefulness of information; however, these two dependent variables have a strong relationship which intervenes in the information adoption process among upper secondary school students.
Pengaruh Model Pembelajaran Teams Games Tournament (Tgt) Berbantuan Media Crossword Puzzle Dan Motivasi Terhadap Prestasi Belajar Siswa Kelas V Pada Matapelajaran Ipa

This research was conducted to answer the issue of whether there is an influence of the learning models of teams games tournament (tgt) assisted by crossword puzzle media and motivation on the learning achievement of fifth grade students in science subjects. The results of the study through the t test showed the prettest data results obtained sig = 0.722, because the significance value of 0.722 > 0.05, the data variance was assumed to be the same. With a sig (2-tailed) value obtained at 0.029 with a sig value of 0.029 Ftable (3.42) then Ho is rejected and Ha is accepted, meaning there is an influence after being given treatment to student learning achievement in science learning. The conclusion of this study is the Teams Games Tournament (TGT) learning model has an effect on student achievement and student motivation.
FOREIGN STUDENTS’ EXPERIENCE IN AN ONLINE INTERNATIONAL MARKET ANALYSIS COURSE IN MEXICO

This study involved 10 European undergraduate students enrolled in the online International Market Analysis (IMA) course offered through the International Business (IB) undergraduate program of CETYS Universidad. Using the Scholarship of Teaching and Learning (SoTL) theoretical approach and a qualitative research design, it asked how the students felt about their online educational experience. The results suggest that the students appreciated the freedom and flexibility that the online course provided. However, the lack of the physical presence of the teacher, and the challenges associated with working independently, made the learning experience less than optimal.
Lexical Patterns Based on Maximal Frequent Secuences for Automatic Keyphrase Extraction

This paper presents a method for the automatic keyphrase extraction task using lexical patterns. First, the patterns are obtained from a set of data and converted into regular expression search patterns, allowing to consider sequences of characters that define a phrase without depending on its syntactic or semantic characteristics and thus obtain a list of possible candidates. Besides, to select the best, only those that obtained a high weight will be considered, in the following four weights: Boolean (B), Precision (P), Recall (R), and F-Measure (F); which corresponds to the result obtained from each evaluated pattern, therefore a list is generating of the best 5,10 and 15 keyphrases for each document. The evaluation of the method was realized by length (L) and combination (C), where the combination takes the best candidates for each length (1 to 4). The method was tested in corpus of scientific articles using the SemEval-2010 data set for task 5.
Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks

Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images. Unfortunately, the same architectures perform much worse when they have to compare parts of an image to each other to correctly classify this image. Until now, no well-formed theoretical argument has been presented to explain this deficiency. In this paper, we will argue that convolutional layers are of little use for such problems, since comparison tasks are global by nature, but convolutional layers are local by design. We will use this insight to reformulate a comparison task into a sorting task and use findings on sorting networks to propose a lower bound for the number of parameters a neural network needs to solve comparison tasks in a generalizable way. We will use this lower bound to argue that attention, as well as iterative/recurrent processing, is needed to prevent a combinatorial explosion.
The Evidence Needed to Adopt Artificial Intelligence into Clinical Practice: Mammography Screening an Exemplar

Mammography screening for breast cancer is an exemplar of an emerging role for artificial intelligence (AI) in image interpretation. Focusing on AI for mammography, the presentation will outline the need to build the evidence base to enable broader adoption of AI in health systems. This includes shifting research focus from early studies of AI development and validation using standardised cancer-enriched imaging datasets, to large scale comparative studies that improve the quality and generalisability of the evidence. Other challenges for the adoption of AI into practice will also be discussed to inform research.
IMPROVING STUDENTS’ ABILITY ON READING COMPREHENSION BY USING DIRECT INSTRUCTION AT NINTH GRADE STUDENTS OF SMP NEGERI 4 DOLOK SANGGUL IN ACADEMIC YEAR 2019/2020

This study aims to improve student’s ability on reading comprehension by using direct instruction. This study was conducted by using classroom action research at the ninth grade of SMP NEGERI 4 DOLOK SANGGUL. The population of this study was the ninth grade of SMP NEGERI 4 DOLOK SANGGUL, the total number of population was 150 students. The sample of this study was about 20% (30 students) by using cluster random sampling  based on Kunto Aji. The researcher was applied in two cycles with six meetings. The data was collected by using the quantitative data and qualitative data. The result showed that the improvement of the student’s score from pretest to post test of cycle I and cycle II. The students’ mean from pretest was 55,4 , for the first cycle was 66,4 , and the second cycle was 78,6. The pretest result 10% of the students got point 70 to up, the competence test cycle I was 36,6% and the improvement of  the students showed in the last competence of the cycle II was 90%.
Memory Representation in Transformer

Transformer-based models have achieved state-of-the-art results in many natural language processing tasks. The self-attention architecture allows transformer to combine information from all elements of a sequence into context-aware representations. However, information about the context is stored mostly in the same element-wise representations. This might limit the processing of properties related to the sequence as a whole more difficult. Adding trainable memory to selectively store local as well as global representations of a sequence is a promising direction to improve the Transformer model. Memory-augmented neural networks (MANNs) extend traditional neural architectures with general-purpose memory for representations. MANNs have demonstrated the capability to learn simple algorithms like Copy or Reverse and can be successfully trained via backpropagation on diverse tasks from question answering to language modeling outperforming RNNs and LSTMs of comparable complexity. In this work, we propose and study few extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, (2) creating memory bottleneck for the global information, (3) controlling memory update with dedicated layer. We evaluate these memory augmented Transformers and demonstrate that presence of memory positively correlates with the model performance for machine translation and language modelling tasks. Augmentation of pre-trained masked language model with memory tokens shows mixed results for tasks from GLUE benchmark. Visualization of attention patterns over the memory suggest that it improves the model's ability to process a global context.
Spatial attention model‐modulated bi‐directional long short‐term memory for unsupervised video summarisation

Compared with surveillance video, user-created videos contain more frequent shot changes, which lead to diversified backgrounds and a wide variety of content. The high redundancy among keyframes is a critical issue for the existing summarising methods in dealing with user-created videos. To address the critical issue, we designed a salient- area-size-based spatial attention model (SAM) on the observation that humans tend to focus on sizable and moving objects in videos. Moreover, the SAM is taken as guidance to refine frame-wise soft selected probability for the bi-directional long short-term memory model. The reinforcement learning framework, trained by the deep deterministic policy gradient algorithm, is adopted to do unsupervised training. Extensive experiments on the SumMe and TVSum datasets demonstrate that our method outperforms the state-of-the-art in terms of F-score.
InfoGuides: SPAN 470 Hispanic Studies Beyond Graduation- Professor Lila McDowell Carlsen: Google Academico

This course will connect Hispanic Studies with other fields of study and the professional world.
Neurally Guided Genetic Programming for Turing Complete Programming by Example

The ability to synthesise source code from input/output examples allows nonexperts to generate programs, and experts to abstract away a wide range of simple programming tasks. Current research in this area has explored neural synthesis, SMT solvers, and genetic programming; each of these approaches is limited, however, often using highly specialised target languages for synthesis. In this paper we present a novel hybrid approach using neural networks to guide genetic programming (GP), which allows us to successfully synthesise code from just ten I/O examples in a generalised Turing complete target language, up to and including a sorting algorithm. We show that GP by itself is able to synthesise a set of simple programs, and show which hints (suggested lines of code for inclusion) are of most utility to GP in solving harder problems. Using a form of unstructured curriculum learning, we then demonstrate that neural networks can be used to determine when to make use of these high-utility hints for specific I/O problems and thus enable complex functions to be successfully synthesised. We apply our approach to two different problem sets: common array-to-array programs (including sorting), and a canvas drawing problem set inspired by So & Oh (2018).
The COVID-19 impacts on contextual privacy behavior in Kuwait

The medical, social and business disturbance associated with the COVID-19 coronavirus caused dramatical changes in the usage of mobile devices People were forced during the lockdown to use different mobile-based context-aware application to cope their lives while staying home Consequently, a typical user's privacy preferences and behavior were altered due to that fact that these applications at that moment are found to be an optimum solution to endure the predicament This paper is aimed to assess how COVID-19 pandemic changes the mobile usage and contextual privacy behavior in Kuwait Toward this goal, an information visualization system is developed to accumulate the user behavior dataset from their mobiles The result shows the average mobile usage hours have increased by 60% and in total the user behavior changed by 27 9% in loosen their contextual privacy © 2020 ACM
Improving accuracy of image-to-physical laparoscopic liver registration via reconstruction of intrahepatic pressure changes from abdominal insufflation

During laparoscopic surgery, insufflation introduces significant deformations to the liver through reshaping the abdominal cavity. Additionally, insufflation is associated with elevation of portal and hepatic venous pressures causing hemodynamic changes that may cause swelling or engorgement of the liver. Current methods for elastic soft tissue registration cannot account for these secondary effects, which have yet remained largely neglected. This paper presents preliminary work towards modeling these intraoperative physiological changes caused by insufflation to improve the accuracy of image-to-physical registration during laparoscopic procedures. Spatially localized modes of internal pressure-driven deformations are introduced to allow reconstruction of intraoperative changes to intrahepatic pressure gradients during registration. A pilot experiment was performed in one sheep to measure volumetric tissue changes induced by insufflation. Liver volume increased by 6.3 ± 0.7% between insufflated and preoperative states, according to three independent raters. Registrations were also performed between organ states, utilizing: (1) rigid registration, (2) deformable registration based on application of external forces to the organ, and (3) the same deformable registration extended to incorporate displacements driven by changes to intrahepatic pressure gradients. Target registration error (TRE) decreased from 15.2 ± 3.2 mm with rigid registration to 5.0 ± 1.9 mm with reconstruction of external boundary forces. When incorporating internal pressure changes, TRE further decreased to 3.0 ± 1.2 mm. Additional analysis comparing a pure scaling approach with the proposed mechanics-based method to account for volume change demonstrated that the method incorporating intrahepatic pressure changes leads to more accurate image-to-physical registrations between the preoperative and insufflated liver.
Convergence or Divergence in EFL Teachers’ and Learners’ Beliefs on Using Smartphones in Learning English: The Case of Master1 Students - University of Tlemcen (Algeria)

The present study attempts to investigate Algerian EFL teachers and learners’ outlooks vis-à-vis the use of smartphones in learning English. The aim from undertaking this study is partially to gain insights into the awareness of Algerian EFL teachers and learners on the importance of integrating smartphones in EFL classroom and partially to find out how much convergent and divergent Algerian teachers and students’ beliefs are in terms of using smartphones in learning English. The study sample consisted of 10 teachers and 30 students of the Department of English in Master 1 level who were enrolled in the academic year of 2019-2020. Data were collected using a questionnaire to examine the students’ beliefs on their use of smartphones in learning English while semi-structured interviews were conducted with teachers. Using the descriptive quantitative design, the findings reveal convergence in teachers’ and students’ responses in that they both show positive attitudes towards using smartphones in learning English. This is because learning English with smartphones results in more motivated students, increased exposure to English, extended vocabulary and easier access to information regardless time and place. Even though, a number of challenges were raised from the part of both teachers and students including the small screen size of their smartphones and slow network connections. On the basis of these results, some pedagogical implications and directions were recommended in the end.
Bi-axis control algorithm for the generation of manuscript shapes from mathematical handwriting model

The study of the human handwriting process shows that bell-shaped velocity profiles are generally observed in the handwriting motion. In this paper, a mathematical model, using input and output data for forearm muscles activities, named ElectroMyoGraphic signals (EMG), and velocities of a pen tip moving on (x, y) plan, is proposed. Using the outputs of this mathematical velocity model, we also propose a novel bi-axis control algorithm to generate handwritten graphic traces. The effectiveness of this approach should be observed on different cursive Arabic letters and geometric shapes constituting the recorded experimental basis.
Drop that Beat! Digital Sampling with Ableton Live in Higher Education (Intermediate)

The aim of this chapter is to get students comfortable with the basics of Ableton live. The lessons described in this chapter were designed for a combined undergraduate and graduate Digital Music Production course<italic>.</italic> Students engage with Ableton Live and a MIDI keyboard to create original beats and sample audio using online databases. Students identify, sample, edit, and manipulate a kick drum, snare drum, hi-hat, and additional percussive sounds using waveform mediums. These lessons use a project-based and teacher-facilitated approach, which allows for scaffolding, modification, and adaptation as needed. Each sequential step outlined can be time-modified based on student needs. This also allows for flexibility of student progress, questions, or challenges that may arise, and individual adaptations for learning.
Automated end-to-end deep learning framework for classification and tumor localization from native non-stained pathology images

Deep learning based quantitative assessment of digital pathology images and understanding the underlying reasons for a specific clinical decision is challenging, and automatic histology pattern classification and tumor localization in whole-slide pathology images are critical for interpretable learning systems. In this study, we propose an end-to-end deep learning framework for automatic detection and localization of tumors directly from non-stained whole slide prostate core biopsy images (WSI). We use a previously described Generative Adversarial Network (GAN)-based model from our laboratory for computational Hematoxylin and Eosin (HE) staining of native non-stained pathology images. A convolutional neural network to detect and classify tumor regions in 1024×1024 virtually stained HE pixel patches, and a concurrent deep weakly supervised (WSL) model that provides localization of predominant histologic patterns used for tumor classification without the need for pixel-level annotations are reported in this study for the first time. The end-to-end system was evaluated on a 17K hold out set of 1024×1024 non-stained patches extracted from 13 whole slide prostate biopsy images. Experimental results yielded 86.37% patch-level classification accuracy with 85.05% precision, and achieved a Dice index of 65.07&plusmn;1.99 (compared to 70.24&plusmn;1.86 Dice index in the U-Net reference model for pixel-level segmentation). The end-to-end deep learning framework thus automates digital pathology image workflow from tissue staining to interpretable prostate tumor classification and can be valuable for accurate grading of prostate cancer and generalized to other whole-slide image classification tasks.
Needs Analysis of English for Specific Purposes at Food Beverage Emersia Hotel Bandar Lampung

The objective of this study is to reveal the language need for hotel staffs in developing English skills based on needs analysis. The participants of this study are three males and two females’ senior staffs. The interview as the instrument to get primary data. The result of this study indicates that speaking and listening are the major need of the staffs. The staffs mostly need speaking and listening skill. Speaking is the most frequently used oral communication to communicate with guests. Speaking also the language functions needed were in greeting, offering menu, asking for the customer's needs or orders, confirmation, dealing with billing, apologizing or dealing with complaining, etc. In addition, some problems are found in listening like listening to accents and dialects. The staffs realize that the accent, the dialect, and the intonation of the guests can have a massive impact of their communication. Therefore, listening is also very needed in their work because listening skill is also needed by the staffs since speaking and listening are interrelated skills. Based on the findings, one suggestion was proposed: to prepare a language training program to improve the English language skills of the staff.