Reviewerly/exHarmony huggingface dataset:
Excess Capacity and Backdoor Poisoning

A backdoor data poisoning attack is an adversarial attack wherein the attacker injects several watermarked, mislabeled training examples into a training set. The watermark does not impact the test-time performance of the model on typical data; however, the model reliably errs on watermarked examples. To gain a better foundational understanding of backdoor data poisoning attacks, we present a formal theoretical framework within which one can discuss backdoor data poisoning attacks for classification problems. We then use this to analyze important statistical and computational issues surrounding these attacks. On the statistical front, we identify a parameter we call the memorization capacity that captures the intrinsic vulnerability of a learning problem to a backdoor attack. This allows us to argue about the robustness of several natural learning problems to backdoor attacks. Our results favoring the attacker involve presenting explicit constructions of backdoor attacks, and our robustness results show that some natural problem settings cannot yield successful backdoor attacks. From a computational standpoint, we show that under certain assumptions, adversarial training can detect the presence of backdoors in a training set. We then show that under similar assumptions, two closely related problems we call backdoor filtering and robust generalization are nearly equivalent. This implies that it is both asymptotically necessary and sufficient to design algorithms that can identify watermarked examples in the training set in order to obtain a learning algorithm that both generalizes well to unseen data and is robust to backdoors.
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling

Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation. Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation. On the other hand, dialog systems still struggle to decide when to change topic. This indicates further research is needed in topic-shift aware dialog modeling.
Pengaruh Media Komik Tematik Terhadap Hasil Belajar Kognitif

Thematic comics are one of the interesting learning media for students. This study aims to determine the effect of thematic comic media on cognitive value learning outcomes. The research was conducted in one of the public elementary schools in Palembang. This research is a quantitative-research with a true-experimental design type, posttest-only control design. The sampling technique used is saturated sampling with the type of probability sampling. The samples used were class II A and class II B. This study used an experimental method with data collection techniques used in this study were interviews, documentation and tests. Data were analyzed using data normality test, homogeneity test, validity test, reliability test and independent sample t-test. The results showed that there was an influence on the cognitive value of students after using thematic comic media, this was indicated by the significant influence of cognitive values ​​on second grade students in one of the public elementary schools in Palembang.
Emotion Recognition under Consideration of the Emotion Component Process Model

Emotion classification in text is typically performed with neural network models which learn to associate linguistic units with emotions. While this often leads to good predictive performance, it does only help to a limited degree to understand how emotions are communicated in various domains. The emotion component process model (CPM) by Scherer (2005) is an interesting approach to explain emotion communication. It states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency. We hypothesize that these components are associated with linguistic realizations: an emotion can be expressed by describing a physiological bodily reaction (he was trembling), or the expression (she smiled), etc. We annotate existing literature and Twitter emotion corpora with emotion component classes and find that emotions on Twitter are predominantly expressed by event descriptions or subjective reports of the feeling, while in literature, authors prefer to describe what characters do, and leave the interpretation to the reader. We further include the CPM in a multitask learning model and find that this supports the emotion categorization. The annotated corpora are available at this https URL.
Personalizing Customer Experience by Implementation of Hybrid Recommendation System

With the rapid use of the internet, a huge amount of data is generated over the network with every passing second whereas, on the other hand, the user demands information that is relevant to his personal search. The processing of such huge data is a challenging task. To serve the user with his specific required information, there is a need for an information retrieval mechanism that can process this large data. A recommendation system is such required technology that retrieves information in order to improve users’ access and thereby recommending items that are relevant to his explicitly mentioned behaviour and preferences. The recommendation algorithm analyses the huge data set and focuses to recommend accurate content to the user. There are several recommendation systems in use today, some popular among them are Netflix, YouTube, Tinder, and Amazon, etc. In this article, discussion is done on various types of recommendation systems, issues of recommendation systems and use cases of widely used recommendation engines and their potential benefits. The work introduced in this paper is an integration approach of domain-specific and item-based recommendation system. The evaluation of the proposed approach is done on Amazon Product Dataset and the performance is measured on evaluation metrics Precision and Recall. Experimental Results prove that the approach introduced in this paper performed well as compared to existing methods.
Literature Survey on Autonomous Rover

In the present era of automation, the Autonomous Rovers have emerged as the prime technology for implementing security and surveillance system which handle human lives. Its potential of being navigated in the environment with no physical devices and human intervention has made it more eminent and efficacious. With the progressive technology, the provision of autonomous vehicles has enhanced their skill to assist and perform in exploration and rescue missions, etc. The survey determines the interpretation of various sensors, actuators and their assimilation for navigation and estimation of the autonomous rover.
Color Multiple Image Encryption Scheme Based on New 3D-Chaotic System

In many research areas, the production of images is increasing every day. These days, the prime focus of researchers is on the security of digital images. Motivated by image encryption algorithms, this paper suggests a novel Chaos-based color multiple image encryption technique. A new 3D-chaotic system has been applied. Confusion/diffusion of image data has been implemented by using sequences generated by A New 3D-chaotic System. In the confusion stage, the input image colored pixels have been scrambled. Then, in the diffusion stage, pixel replacement is done by image sequences of the new system .To increase the complexity and security of the image, the XOR process was performed. The proposed scheme efficiency is validated via key sensitivity, key space, entropy, horizontal, vertical, diagonal correlation, UACI and NPCR tests. Furthermore, the overall security analysis and experimental results show that the proposed encryption technique has achieved confidentiality and has resistance to attacks.
Learning to Synthesize Programs as Interpretable and Generalizable Policies

Recently, deep reinforcement learning (DRL) methods have achieved impressive performance on tasks in a variety of domains. However, neural network policies produced with DRL methods are not human-interpretable and often have difficulty generalizing to novel scenarios. To address these issues, prior works explore learning programmatic policies that are more interpretable and structured for generalization. Yet, these works either employ limited policy representations (e.g. decision trees, state machines, or predefined program templates) or require stronger supervision (e.g. input/output state pairs or expert demonstrations). We present a framework that instead learns to synthesize a program, which details the procedure to solve a task in a flexible and expressive manner, solely from reward signals. To alleviate the difficulty of learning to compose programs to induce the desired agent behavior from scratch, we propose to first learn a program embedding space that continuously parameterizes diverse behaviors in an unsupervised manner and then search over the learned program embedding space to yield a program that maximizes the return for a given task. Experimental results demonstrate that the proposed framework not only learns to reliably synthesize task-solving programs but also outperforms DRL and program synthesis baselines while producing interpretable and more generalizable policies. We also justify the necessity of the proposed two-stage learning scheme as well as analyze various methods for learning the program embedding.
Nuisance-Label Supervision: Robustness Improvement by Free Labels

In this paper, we present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them. For example, an activity recognition model should perform consistently regardless of the change of clothes and background. But our experiments show existing models are far from this capability. So we explicitly supervise a model with nuisance labels to make extracted features less dependent on nuisance factors. Although the values of nuisance factors are rarely annotated, we demonstrate that besides existing annotations, nuisance labels can be acquired freely from data augmentation and synthetic data. Experiments show consistent improvement in robustness towards image corruption and appearance change in action recognition.
Analisis Faktor Penyebab Pengulangan Citra DigitalMenggunakan Computed Radiography (CR) di Instalasi Radiologi RSI Sultan Agung Semarang

Background : At the Radiology Installation of RSI Sultan Agung Semarang, currently using CR which supports digital image repetition analysis, but until now it hasn’t been done. In the Radiology Installation of Sultan Agung Hospital in Semarang that has been applied is the image rejection analysis on hardcopy or printed films. The purpose of this study is to know the procedure, the percentage of the analysis result digital image repetition, and the problem solving of repeat analysis in Radiology Installation of RSI Sultan Agung Semarang.Methods : The method is survey research with quantitative approach. The periode of collecttion used data on February 2019 – April 2019 at Radiology Installation of RSI Sultan Agung Semarang, by taking a sample that is repetition of a digital image of the overall radiological examination both non-contrast or contrast using CR modalities. Data collection methods with obsevation, documentation and FGD. then data have been analyzed based on the factors causing image repeatition, and make a pareto diagram to find out the dominant factors that cause repetition, and make fishbone diagrams to determine image repetition solutions.Results : The results of this study indicate that the radiology installation does not have a digital image rejection analysis procedure. The percentage results show that the total repetition of images from February to April 2019 is 6.55 %. The main causative factors are the patient's position with a percentage of 43.98% and the type of examination with the most repetitions is Abdomen with a percentage of 18.38%. The solution to suppressing repetition rates is to apply a program of analysis of factors that cause image repetition in CR modalities which so far has not been implemented in the Radiology Installation of RSI Sultan Agung Semarang and make preventive efforts to prevent the occurrence of repetition of images based on their respective factors.
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random cropping and color jittering. However, such invariance could be harmful to downstream tasks if they rely on the characteristics of the data augmentations, e.g., location- or color-sensitive. This is not an issue just for unsupervised learning; we found that this occurs even in supervised learning because it also learns to predict the same label for all augmented samples of an instance. To avoid such failures and obtain more generalizable representations, we suggest to optimize an auxiliary self-supervised loss, coined AugSelf, that learns the difference of augmentation parameters (e.g., cropping positions, color adjustment intensities) between two randomly augmented samples. Our intuition is that AugSelf encourages to preserve augmentation-aware information in learned representations, which could be beneficial for their transferability. Furthermore, AugSelf can easily be incorporated into recent state-of-the-art representation learning methods with a negligible additional training cost. Extensive experiments demonstrate that our simple idea consistently improves the transferability of representations learned by supervised and unsupervised methods in various transfer learning scenarios. The code is available at this https URL
Smart Insoles: Prevention of Falls in Older People through Instant Risk Analysis and Signalling

Falls in the elderly are a leading cause of injury, affecting one in three older adults annually. These fall incidents can lead to various disabling conditions, and therefore have the ability to affect one’s quality of life and independence. Not only are falls potentially taxing to the individual, they are also responsible for a big portion of annual health care costs. Due to an ageing society, the number of falls and their consequences will grow in parallel with the expanding number of seniors, becoming an even greater concern for the health care system. Therefore, there is an ever-increasing need to develop (cost-)effective fall prediction systems to reduce these financial and physical burdens associated with the consequences of a fall. Introducing the design proposal of my graduation thesis: smart insoles with accessory mobile application, the ‘StApp’. The insoles with an integrated fall prediction smart system capture the physiological risk factor of an impaired minimum toe clearance (MTC) variable, signal the impending danger to the user, and hence, aid falls prevention in the elderly. The final product consists of three segments, detect - warn - and inform, represented in the two separate products. (Detect) Smart insoles that analyse and monitor the user’s gait in real-time, in particular the MTC parameter, through a sensing technology integrated in the sole. (Warn) When MTC values display an apparent risk, the user is alerted through a technological intervention. This presents itself in the form of vibrational stimulus, embedded in the support arch of the smart insole. Hereby, the whole smart system, including the intervention, is embedded in the smart insole, discreetly concealed. (Inform) For the sensor data to be meaningful to the user, an accessory mobile application monitoring the MTC- and multiple other gait parameters is suggested. The StApp presents concrete information and advice on sufficient and safe exercise to improve the user’s MTC and overall gait and balance. An experimental research study was conducted to preliminarily validate the viability of the main product-idea of the smart insoles, studying the effect of the product’s intervention. A minimum of 2 minutes of treadmill walking was analysed for two young adults (1 M, 1 F) using the Tracker motion analysis software. The effect of the intervention was clearly illustrated in the data gathered in this pilot test, as the effect of the intervention deemed to be statistically for all administered vibrational stimuli. It seems to be an appropriate tool to optimize the toe clearance parameters in the pursuit for strategies of falls prevention. However, further in-depth research is required to explore the intervention’s characteristics and study the effect of the intervention on the elderly’s gait. Finally, as the wearable technology depicted enables optimization of elderly care, healthcare professionals who might be interested in giving more personalized advice to their patients will benefit from the design of the smart insole and accessory StApp, presented in this project.
Development of Design Thinking Class Program Applying Creative Problem Solving(CPS) in College Classes

The purpose of this study is to develop a design thinking program that applies creative problem solving (hereafter CPS) in college classes. As a research method to achieve the purpose of this study, first, through literature and case studies, the core value of the design thinking class program applying CPS and the core learning activity process were derived. In addition, the blending design strategy was established by finding the core support elements required for each process, and the draft program was completed. Second, the draft program was revised and supplemented through expert formative evaluation, and the final improved program was completed. Third, to verify the feasibility of the completed program, a validation test(CVI) was conducted from a group of experts. As a result of the validation test(CVI), the design thinking program for creative problem solving was positively evaluated as having explanatory power, usefulness, validity, understanding, and universality. The results of this study will provide a guide for creative and systematic learning to professors who want to use CPS-applied design thinking in various on- and off-line environments.
Deep Neural Network for Speaker Identification Using Static and Dynamic Prosodic Feature for Spontaneous and Dictated Data

We can recognize a person by his voice alone. In principle, the sound has a tone (pitch) that is different for each person. This study aims to measure a Deep Neural Network (DNN) performance with static and dynamic prosodic features. Prosodic is information about sound related to tone, intonation, pressure, duration, and rhythm of a person's pronunciation. The data used is dictated and spontaneous voice data that taken from YouTube. The data used consists of three male voices and one female voice. The data is segmented into various duration, 3 seconds, 5 seconds, and 10 seconds. After the data has been segmented, the static prosodic features with 103 dimensions will be extracted and the dynamic prosodic features with 13 dimensions will be extracted too. Each feature and feature combination will be trained and tested using DNN with a ratio of 90:10. The result shows that the 10 seconds segmented data has higher accuracy than the others. Accuracy of static prosodic features is better than dynamic prosodic features. The average accuracy of DNN for static prosodic features is 87.02%. The average accuracy of DNN for dynamic prosodic features is 72.97%. The average accuracy of DNN for combined static and dynamic prosodic features is 87.72%.
On the Estimation Bias in Double Q-Learning

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operator. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value prediction and improving learning performance. However, as shown by prior work, double Q-learning is not fully unbiased and still suffers from underestimation bias. In this paper, we show that such underestimation bias may lead to multiple non-optimal fixed points under an approximated Bellman operation. To address the concerns of converging to non-optimal stationary solutions, we propose a simple and effective approach as a partial fix for underestimation bias in double Q-learning. This approach leverages real returns to bound the target value. We extensively evaluate the proposed method in the Atari benchmark tasks and demonstrate its significant improvement over baseline algorithms.
Web Services for Sentiment Analysis on Social Media Websites

Since there are so many types of services available for web, it is hard to determine a service that will work best to analyze social media websites for the extremist content. An approach is developed for testing each dataset that is extracted according to the keyword extremism by working with APIs of web services utilizing PHP programming language. In making the comparison for web services, recall, precision, accuracy, and f-measures are analyzed to determine the service with the lowest mean square error (MSE). Four sentiment analysis web services are being used: Aylien, Sentiment Analyzer, Monkey Learn, and Parallel Dots. Monkey Learn scored lowest on MSE of 14% among all web services in our comparison of all web services.
A Theoretical Analysis of Fine-tuning with Linear Teachers

Fine-tuning is a common practice in deep learning, achieving excellent generalization results on downstream tasks using relatively little training data. Although widely used in practice, it is lacking strong theoretical understanding. We analyze the sample complexity of this scheme for regression with linear teachers in several architectures. Intuitively, the success of fine-tuning depends on the similarity between the source tasks and the target task, however measuring it is non trivial. We show that a relevant measure considers the relation between the source task, the target task and the covariance structure of the target data. In the setting of linear regression, we show that under realistic settings a substantial sample complexity reduction is plausible when the above measure is low. For deep linear regression, we present a novel result regarding the inductive bias of gradient-based training when the network is initialized with pretrained weights. Using this result we show that the similarity measure for this setting is also affected by the depth of the network. We further present results on shallow ReLU models, and analyze the dependence of sample complexity there on source and target tasks. We empirically demonstrate our results for both synthetic and realistic data.