Reviewerly/exHarmony huggingface dataset:
Measuring Generalization and Overfitting in Machine Learning

Author(s): Roelofs, Rebecca | Advisor(s): Recht, Benjamin; Demmel, James | Abstract: Due to the prevalence of machine learning algorithms and the potential for their decisions to profoundly impact billions of human lives, it is crucial that they are robust, reliable, and understandable. This thesis examines key theoretical pillars of machine learning surrounding generalization and overfitting, and tests the extent to which empirical behavior matches existing theory. We develop novel methods for measuring overfitting and generalization, and we characterize how reproducible observed behavior is across differences in optimization algorithm, dataset, task, evaluation metric, and domain.First, we examine how optimization algorithms bias machine learning models towards solutions with varying generalization properties. We show that adaptive gradient methods empirically find solutions with inferior generalization behavior compared to those found by stochastic gradient descent. We then construct an example using a simple overparameterized model that corroborates the algorithms’ empirical behavior on neural networks. Next, we study the extent to which machine learning models have overfit to commonly reused datasets in both academic benchmarks and machine learning competitions. We build new test sets for the CIFAR-10 and ImageNet datasets and evaluate a broad range of classification models on the new datasets. All models experience a drop in accuracy, which indicates that current accuracy numbers are susceptible to even minute natural variations in the data distribution. Surprisingly, despite several years of adaptively selecting the models to perform well on these competitive benchmarks, we find no evidence of overfitting. We then analyze data from the machine learning platform Kaggle and find little evidence of substantial overfitting in ML competitions. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts.Overall, our work suggests that the true concern for robust machine learning is distribution shift rather than overfitting, and designing models that still work reliably in dynamic environments is a challenging but necessary undertaking.
SecDATAVIEW

Big data workflow management systems (BDWFMSs) have recently emerged as popular platforms to perform large-scale data analytics in the cloud. However, the protection of data confidentiality and secure execution of workflow applications remains an important and challenging problem. Although a few data analytics systems were developed to address this problem, they are limited to specific structures such as Map-Reduce-style workflows and SQL queries. This paper proposes SecDATAVIEW, a BDWFMS that leverages Intel Software Guard eXtensions (SGX) and AMD Secure Encrypted Virtualization (SEV) to develop a heterogeneous trusted execution environment for workflows. SecDATAVIEW aims to (1) provide the confidentiality and integrity of code and data for workflows running on public untrusted clouds, (2) minimize the TCB size for a BDWFMS, (3) enable the trade-off between security and performance for workflows, and (4) support the execution of Java-based workflow tasks in SGX. Our experimental results show that SecDATAVIEW imposes 1.69x to 2.62x overhead on workflow execution time on SGX worker nodes, 1.04x to 1.29x overhead on SEV worker nodes, and 1.20x to 1.43x overhead on a heterogeneous setting in which both SGX and SEV worker nodes are used.
Analysis on Different Strategies Used in Blockchain Technology

Blockchain technology is a distributed ledger, it’s reliable, transparent, secure, trust-worthy, confidential and authenticate. Blockchain is a combination of blocks which contains the header of valid transactions using one of the best hashing algorithm (SHA-256 bits). Different strategies will be used in blockchain i.e., private blockchain, public (Example: Ethereum and bitcoins) and consortium blockchain. In this paper, we discuss about basic process of E-voting, IOT with blockchain, financing and blockchain technology helps for providing highest performance in rural areas.
What kind of emotions do emoticons communicate?

Purpose The purpose of this paper is to study the nature and concept of emoticons/emojis. Instead of taking for granted that these user-generated formats are necessarily emotional, we empirically assessed in what extent are they and the specificity of each one. Drawing on congruent mood state, valence core and emotion appraisal theories we expected a compatible statistical association between positive/negative/neutral emotional valence expressions and emoticons of similar valence. The positive emoticons were consistently associated with positive valence posts. Added to that analysis, 21 emotional categories were identified in posts and correlated with eight emoticons. Design/methodology/approach Two studies were used to address this question. The first study defined emoticon concept and interpreted their meaning highlighting their communication goals and anticipated effects. The link between emojis and emoticons was also obtained. Some emoticons types present more ambiguity than others. In the second study, three years of real and private (Facebook) posts from 82 adolescents were content analyzed and coded. Findings Only the neutral emoticons always matched neutral emotional categories found in the written interaction. Although the emoticon valence and emotional category congruence pattern was the rule, we also detected a combination of different valence emoticons types and emotion categories valence expressions. Apparently the connection between emoticon and emotion are not so obviously straightforward as the literature used to assume. The created objects designed to communicate emotions (emoticons) have their specific corresponding logic with the emotional tone of the message. Originality/value Theoretically, we discussed the emotional content of emoticons/emojis. Although this king of signals have an Asian origin and later borrowed from the western countries, their ambiguity and differing specificity have never been analyzed.
THE DIGITAL COMPETENCE ACTIONS FRAMEWORK

1 Aristotelion University of Thessaloniki and University of Macedonia (GREECE)2 University of Macedonia (GREECE)
Spy game: verifying a local generic solver in Iris

We verify the partial correctness of a "local generic solver", that is, an on-demand, incremental, memoizing least fixed point computation algorithm. The verification is carried out in Iris, a modern breed of concurrent separation logic. The specification is simple: the solver computes the optimal least fixed point of a system of monotone equations. Although the solver relies on mutable internal state for memoization and for "spying", a form of dynamic dependency discovery, it is apparently pure: no side effects are mentioned in its specification. As auxiliary contributions, we provide several illustrations of the use of prophecy variables, a novel feature of Iris; we establish a restricted form of the infinitary conjunction rule; and we provide a specification and proof of Longley's modulus function, an archetypical example of spying.
An enhanced lossless compression with cryptography hybrid mechanism for ECG biomedical signal monitoring

Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset.
Quality Estimation and Optimization of Adaptive Stereo Matching Algorithms for Smart Vehicles

Stereo matching is a promising approach for smart vehicles to find the depth of nearby objects. Transforming a traditional stereo matching algorithm to its adaptive version has potential advantages to achieve the maximum quality (depth accuracy) in a best-effort manner. However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage/frequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road.
Double speckle pattern interferometric measurements for micro-angular displacement and the center of rotation

Double speckle pattern interferometry is presented for the measurement of in-plane rotation angle, sign, and center of rotation. The technique employs two conventional in-plane sensitive electronic speckle pattern interferometry systems combined with two-wavelength laser illumination and a phase-shifting method. Angular displacement of micro-rotation including the sign is determined from the wrapped phase difference, and the center of rotation is located by using wrapped phase difference maps related to two-directional displacement. The test setup is described and experimental results indicate that the system can provide angular displacement measurement with accuracy of 1.8 arcsec.
No-Reference Video Quality Assessment using Multi-Level Spatially Pooled Features

Video Quality Assessment (VQA) methods have been designed with a focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild. Deep learning approaches have had limited success due to the small size and diversity of existing VQA datasets, either artificial or authentically distorted. We introduce a new in-the-wild VQA dataset that is substantially larger and diverse: FlickrVid-150k. It consists of a coarsely annotated set of 153,841 videos having 5 quality ratings each, and 1600 videos with a minimum of 89 ratings each. Additionally, we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level spatially pooled deep features (MLSP). They are extremely well suited for training at scale, compared to deep transfer learning approaches. Our best method MLSP-VQA-FF improves the Spearman Rank-order Correlation Coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model (0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC). We further investigate how alternative approaches perform under different levels of label noise, and dataset size, showing that MLSP-VQA-FF is the overall best method. Finally, we show that MLSP-VQA-FF trained on FlickrVid-150k sets the new state-of-the-art for cross-test performance on KonVid-1k and LIVE-Qualcomm with a 0.79 and 0.58 SRCC, respectively, showing excellent generalization.
Outlier Detection Algorithm Based on Gaussian Mixture Model

Outlier detection is an important aspect in the field of data mining. In order to solve the problem of outlier detection in high-dimensional datasets, an outlier detection algorithm based on Gaussian mixture model is proposed. First of all, for the data set to be tested, the global optimization expectation maximization algorithm is used to fit a Gaussian mixture model, and then the three-time standard deviation principle is introduced on each Gaussian component, the outlier is the data point outside the range of the mean deviation of the mean value of three times the standard deviation. Through the experiments on the simulation dataset and the real data set, the effectiveness of the algorithm on the outlier detection of high-dimensional data sets is verified.
AUTOMATIC LOCALIZATION OF LANDMARKS IN CEPHALOMETRIC IMAGES Via MODIFIED U-Net

Cephalometric analysis is basic assessment aid for orthodontics, oral & maxillofacial surgery and treatment planning. The identification of landmark locations on lateral cephalograms plays a critical role in the clinical analysis, automating this process could reduce possible human errors and time consumption. This work explores the automation of landmark detection process using deep learning framework. The modified U-Net model has been trained on seven landmarks and implemented to locate the same in test images. The proposed model has been evaluated with dice metrics which was observed to be approximately 88 percent for each landmark. The successful detection rate has been calculated and compared with two other prominent methods, comparable results were achieved which comes under clinically acceptable range. This work is in the early stages as not many deep learning methods explored in this particular domain, which demonstrates assurance for improvement in computer-aided treatment and surgery planning.
Implementation of Naive Bayes Algorithm for Spam Comments Classification on Instagram

Instagram is one of the popular social media in Indonesia, even in the world. With instagram, users can share their moments of life in the form of photos or videos. Instagram users can follow each other. But when a user already has a lot of followers, many instagram accounts also respond to posts with comments that can be categorized as spam. Spam comments are usually found in every account post that has a lot of followers, especially public figures in Indonesia and of course this is very annoying. Instagram has provided services to delete or hide comments, but a model is still needed to detect comments that are spam or notspam.The Naive Bayes algorithm will look for the probability of each class when the comments are inputed. Before the comments probability are calculated for each class, comments will be processed through the preprocessing stage, namely casefolding, cleaning, tokenizing, and stemming. After knowing the probability value of each class, then the probability value will be compared. If the highest probability value is a comment that is hypothesized as spam class, then the comment will be labeled as spam. If the highest probability value is a comment that is hypothesized as notspam class, then the comment will be labeled as notspam.Model test result : F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -measure of 0.83,recall of 0.98 and, precision of 0.72, It can be concluded that the classification of spam comments on Instagram in this research was successful.
Analysis of Part of Speech Tags in Language Identification of Code-Mixed Text

Language identification is the detection of the language of a text in which it is written. The problem becomes challenging when the writer does not use the indigenous script of a language. Generally, this kind of text is generated by social media which is a mixture of English with the native language(s) of the writer. The users of social media platforms that belong to India write in code-mixed Hindi–English language. In this work, we study the word-level language identification as a classification problem to identify the language of a word written in Roman script. We employ POS tags in a transliteration-based approach to prepare the Hindi–English code-mixed corpus. We evaluate the corpus over itself and observe that notable results are obtained.
A Study on Improved JPEG Steganography Algorithm to Prevent Steganalysis

Video steganography is a data-hiding technology that inserts confidential messages intended to be hidden in video data at an unrecognizable level. Image steganography can be divided into spatial area techniques for inserting confidential data by manipulating the Last Significant Bit (LSB) of each image pixel and frequency domain techniques for manipulating the DCT (Discrete Cosine Transform) coefficients of images. JPEG (Joint Photographic Experts Group), which is used as the image loss compression standard, uses JPEG steganography techniques such as Jsteg, F3, F4, and F5 based on frequency domain techniques that manipulate DCT coefficients. Each technique has the characteristic that the frequency of DCT coefficient of 0 increases compared to the original. Various techniques are presented to determine JPEG Stego images by machine learning the characteristics of the relationship between image complexity and frequency of DCT coefficient 0. Therefore, this paper proposes a JPEG steganography algorithm to prevent the determination of the Stego image using DCT coefficient 0 and maintain an even coefficient distribution based on the JPEG characteristic, DCT coefficient 0.
Regression and Classification in Supervised Learning

The problem of recognizing patterns from big data has attracted a lot of attention these days, especially in artificial intelligence and machine learning fields. People are interested in training computers to make predictions or classifications on their own based on past experience, i.e., data. In this paper, we review three fundamental supervised learning models (linear regression, logistic regression, and perceptron) for both regression and classification tasks, including their theoretical background, algorithmic solutions, and application scenarios. We also conduct synthetic experiments to demonstrate their performance.
A Study on the Understanding Analysis of NCS-based Onboard Training Curriculum about Navigation of Maritime High School

The NCS is developed for the purpose of acquiring skills necessary in actual working fields. It is applied in education and training of Specialized high schools and Meister schools. These maritime high schools organize and apply the NCS-based onboard training curriculum for students in the first semester of the second year. In this research, the surveys were conducted three times with the aim of analyzing students’ comprehension of the applied NCS on board. Based on the results of the surveys, Repeated measurement ANOVA was performed to identify the improvement of students’ comprehension on a periodic basis. In addition, One-way ANOVA was performed to identify the difference in understanding of the competency units in each of the education sessions. Statistical analysis was performed below the significance level of 0.05. As the results, the average comprehension of the competency units of the first session turns out to be 1.651/5.000, the second session 3.015/5.000, and the third session 4.422/5.000. Also it has shown that the improvement of understanding and comprehension increased in tandem with the period the students spent on board. Notably, comprehension of the education contents was significantly enhanced after four months of the onboard period. At the end of the training session, the overall understanding of all competency units stood out high, but the figures of Position of fixing unit and Care of cargo unit were relatively low compared with the other competencies. This research is believed to contribute in restructuring the NCS-based onboard training curriculums and the enhancement of the teaching method.
Constrained Markov Decision Processes via Backward Value Functions

Although Reinforcement Learning (RL) algorithms have found tremendous success in simulated domains, they often cannot directly be applied to physical systems, especially in cases where there are hard constraints to satisfy (e.g. on safety or resources). In standard RL, the agent is incentivized to explore any behavior as long as it maximizes rewards, but in the real world, undesired behavior can damage either the system or the agent in a way that breaks the learning process itself. In this work, we model the problem of learning with constraints as a Constrained Markov Decision Process and provide a new on-policy formulation for solving it. A key contribution of our approach is to translate cumulative cost constraints into state-based constraints. Through this, we define a safe policy improvement method which maximizes returns while ensuring that the constraints are satisfied at every step. We provide theoretical guarantees under which the agent converges while ensuring safety over the course of training. We also highlight the computational advantages of this approach. The effectiveness of our approach is demonstrated on safe navigation tasks and in safety-constrained versions of MuJoCo environments, with deep neural networks.
Application of Advanced Encryption Standard on Real Time Secured Voice Communication using FPGA

This paper proposes an application of AES on real time secured voice communication system using FPGA (Field-Programmable Gate Array). The propose system consist of Micro-Blaze., UART control, 128-bit key encryption and GPIO. Key provides substantiation and takeover verification. Encryption is employed for security of voice communication. The cryptography is combined with the voice communication hardware that provides absolutely the distinctive benefit of highest security with one and only excellent voice quality and unnoticeable local area communication unique security features. For encryption the data, FPGA is an adequate device. For 128-bit AES application on real time secure voice communication system, we have used Nexys4 DDR board. Algorithm is designed in Verilog and VHDL, and after realization is done on Nexys4 DDR board the secure data is wirelessly transmitted through the Pmod RF module. The transmitted data has been received by another Pmod RF module and decrypted data realization on the same type of another FPGA board.
Considerations for using distributed research networks to conduct aspects of randomized trials

Stakeholders in the clinical research enterprise are aligned around the need to make clinical research in general, and randomized controlled trials in particular, more meaningful and efficient. To that end, we have built distributed research networks (DRNs) for the Sentinel System, the National Institutes of Health (NIH) Collaboratory, and the National Patient-Centered Clinical Research Network (PCORnet). DRNs reuse electronic health record (EHR) and claims data for research. The design and use of health data DRNs is complicated by lack of uniformity in data collection, a fragmented healthcare system, and the imperative to protect research participants. We describe the key elements of successful DRNs, as well as methods, challenges, and solutions we have encountered in using DRNs to support different phases of randomized, multi-site, clinical research. This work supports “real-world” efforts to build a learning health system and will enable others to conduct randomized clinical trial research using a DRN.