nils-herrmann/clean_scirepeval huggingface dataset:
Cs246: Mining Massive Data Sets Problem Set 3 General Instructions Questions 1 Latent Features for Recommendations (25 Points)

Only one late period is allowed for this homework (11:59pm 2/23).  These questions require thought but do not require long answers. Please be as concise as possible. You should submit your answers as a writeup in PDF format via GradeScope and code via the Snap submission site. Submitting writeup: Prepare answers to the homework questions into a single PDF file and submit it via http://gradescope.com. Make sure that the answer to each question is on a separate page. On top of each page write the number of the question you are answering. Please find the cover sheet and the recommended templates located here: Not including the cover sheet in your submission will result in a 2-point penalty. It is also important to tag your answers correctly on Gradescope. We will deduct 5/N points for each incorrectly tagged subproblem (where N is the number of subproblems). This means you can lose up to 5 points for incorrect tagging. Put all the code for a single question into a single file and upload it.  This problem requires substantial computing time (it can be a few hours on some systems). Don't start it at the last minute. 7 7 7 The goal of this problem is to implement the Stochastic Gradient Descent algorithm to build a Latent Factor Recommendation system. We can use it to recommend movies to users.
MARKETING EFFECTIVENESS BY WAY OF METRICS

Every company should be able to demonstrate own efficiency and effectiveness by used metrics or other processes and standards. Businesses may be missing a direct comparison with competitors in the industry, which is only possible using appropriately chosen instruments, whether financial or non-financial. The main purpose of this study is to describe and compare the approaches of the individual authors. to find metric from reviewed studies which organization use to measuring own marketing activities with following separating into financial metrics and non-financial metrics. The paper presents advance in useable metrics, especially financial and non-financial metrics. Selected studies, focusing on different branches and different metrics, were analyzed by the authors. The results of the study is describing relevant metrics to prove efficiency in varied types of organizations in connection with marketing effectiveness. The studies also outline the potential methods for further research focusing on the application of metrics in a diverse environment. The study contributes to a clearer idea of how to measure performance and effectiveness.
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper

Feature selection is often an essential data processing step prior to applying a learning algorithm The re moval of irrelevant and redundant information often improves the performance of machine learning algo rithms There are two common approaches a wrapper uses the intended learning algorithm itself to evaluate the usefulness of features while a lter evaluates fea tures according to heuristics based on general charac teristics of the data The wrapper approach is generally considered to produce better feature subsets but runs much more slowly than a lter This paper describes a new lter approach to feature selection that uses a correlation based heuristic to evaluate the worth of fea ture subsets When applied as a data preprocessing step for two common machine learning algorithms the new method compares favourably with the wrapper but re quires much less computation
Multidimensional Predicates for Prolog

In 2014, Ungar et al. proposed Korz, a new computational model for structuring adaptive (object-oriented) systems [UOK14]. Korz combines implicit parameters and multiple dispatch to structure the behavior of objects in a multidimensional space. Korz is a simple yet expressive model which does not require special programming techniques such as the Visitor or Strategy pattern to accommodate a system for emerging contextual requirements. We show how the ideas of Korz can be integrated in a Prolog system by extending its syntax and semantics with simple meta-programming techniques. We developed a library, called mdp (multidi-mensional predicates) which can be used to experiment with multidimensional Prolog systems. We highlight its benefits with numerous scenarios such as printing debugging information, memoization, object-oriented programming and adaptive GUIs. In particular, we point out that we can structure and extend Prolog programs with additional concerns in a clear and concise manner. We also demonstrate how Prolog's unique meta-programming capabilities allow for quick experimentation with syntactical and semantical enhancement of the new, multidimensional model. While there are many open concerns, such as efficiency and comprehensibility in the case of larger systems, we will see that we can use the leverage of mdp and Prolog to explore new horizons in the design of adaptive systems.
Optimized Image Steganography using Discrete Wavelet Transform ( DWT )

-Steganography is a term used for covered writing. Steganography can be applied on different file formats, such as audio, video, text, image etc. In image steganography, data in the form of image is hidden under some image by using transformations such as ztransformation, integer wavelet transformation, DWT etc and then sent to the destination. At the destination, the data is extracted from the cover image using the inverse transformation. This paper presents a new approach for image steganography using DWT. The cover image is divided into higher and lower frequency sub-bands and data is embedded into higher frequency sub-bands.  Transformation is used to increase the security. The proposed approach is implemented in MATLAB 7.0 and evaluated on the basis of PSNR, capacity and correlation. The proposed approach results in high capacity image steganography as compared to existing approaches. Image Steganography, PSNR, Discrete Wavelet Transform.
Quantitative Analysis of Smart Contracts

Smart contracts are computer programs that are executed by a network of mutually distrusting agents, without the need of an external trusted authority. Smart contracts handle and transfer assets of considerable value (in the form of crypto-currency like Bitcoin). Hence, it is crucial that their implementation is bug-free. We identify the utility (or expected payoff) of interacting with such smart contracts as the basic and canonical quantitative property for such contracts. We present a framework for such quantitative analysis of smart contracts. Such a formal framework poses new and novel research challenges in programming languages, as it requires modeling of game-theoretic aspects to analyze incentives for deviation from honest behavior and modeling utilities which are not specified as standard temporal properties such as safety and termination. While game-theoretic incentives have been analyzed in the security community, their analysis has been restricted to the very special case of stateless games. However, to analyze smart contracts, stateful analysis is required as it must account for the different program states of the protocol. Our main contributions are as follows: we present (i) a simplified programming language for smart contracts; (ii) an automatic translation of the programs to state-based games; (iii) an abstractionrefinement approach to solve such games; and (iv) experimental results on real-world-inspired smart contracts.
Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.
Supplementation of a Standardized Extract from Phyllanthus emblica Improves Cardiovascular Risk Factors and Platelet Aggregation in Overweight/Class-1 Obese Adults

The objective of this study (clinicaltrials.gov NCT01858376) was to determine the effect of oral supplementation of a standardized extract of Phyllanthus emblica (CAPROS(®)) on cardiovascular disease (CVD) risk factors in overweight adult human subjects from the US population. Class-1 obese (body-mass index: 25-35) adult subjects received 500 mg of CAPROS supplement b.i.d for 12 weeks. The study design included two baseline visits followed by 12 weeks of supplementation and then 2 weeks of washout. At all visits, peripheral venous blood was collected in sodium citrate tubes. Lipid profile measurements demonstrated a significant decrease in calculated low-density lipoprotein cholesterol and total cholesterol/high-density lipoprotein following 12 weeks of CAPROS supplementation when compared to averaged baseline visits. Circulatory high-sensitivity C reactive protein (hs-CRP) levels were significantly decreased after 12 weeks of supplementation. In addition, both ADP- and collagen-induced platelet aggregation was significantly downregulated following 12 weeks of supplementation. Overall, the study suggests that oral CAPROS supplementation may provide beneficial effects in overweight/Class-1 obese adults by lowering multiple global CVD risk factors.
Evaluating Reliability and Predictive Validity of the Persian Translation of Quantitative Checklist for Autism in Toddlers (Q-CHAT)

Early screening of autism increases the chance of receiving timely intervention. Using the Parent Report Questionnaires is effective in screening autism. The Q-CHAT is a new instrument that has shown several advantages than other screening tools. Because there is no adequate tool for the early screening of autistic traits in Iranian children, we aimed to investigate the adequacy of the Persian translation of Q-CHAT.



At first, we prepared the Persian translation of the Quantitative Checklist for Autism in Toddlers (Q-CHAT). After that, an appropriate sample was selected and the check list was administered. Our sample included 100 children in two groups (typically developing and autistic children) who had been selected conveniently. Pearson's r was used to determine test-retest reliability, and Cronbach's alpha coefficient was used to explore the internal consistency of Q-CHAT. We used the receiver operating characteristics curve (ROC) to investigate whether Q-CHAT can adequately discriminate between typically developing and ASD children or not. Data analysis was carried out by SPSS 19.



The typically developing group consisted of 50 children with the mean age of 27.14 months, and the ASD group included50 children with the mean age of 29.62 months. The mean of the total score for the typically developing group was 22.4 (SD=6.26) on Q-CHAT and it was 50.94 (SD=12.35) for the ASD group, which was significantly different (p=0.00).The Cronbach's alpha coefficient of the checklist was 0.886, and test-retest reliability was calculated as 0.997 (p<0.01). The estimated area under the curve (AUC) was 0.971. It seems that the total score equal to 30 can be a good cut point to identify toddlers who are at risk of autism (sensitivity= 0.96 and specificity= 0.90).



The Persian translation of Q-CHAT has good reliability and predictive validity and can be used as a screening tool to detect 18 to 24 months old children who are at risk of autism.
Modified Incremental Conductance Algorithm for Photovoltaic System Under Partial Shading Conditions and Load Variation

Under partial shading conditions, multiple peaks are observed in the power-voltage (P- V) characteristic curve of a photovoltaic (PV) array, and the conventional maximum power point tracking (MPPT) algorithms may fail to track the global maximum power point (GMPP). Therefore, this paper proposes a modified incremental conductance (Inc Cond) algorithm that is able to track the GMPP under partial shading conditions and load variation. A novel algorithm is introduced to modulate the duty cycle of the dc-dc converter in order to ensure fast MPPT process. Simulation and hardware implementation are carried out to evaluate the effectiveness of the proposed algorithm under partial shading and load variation. The results show that the proposed algorithm is able to track the GMPP accurately under different types of partial shading conditions, and the response during variation of load and solar irradiation are faster than the conventional Inc Cond algorithm. Hence, the effectiveness of the proposed algorithm under partial shading condition and load variation is validated in this paper.
Pyramid Scene Parsing Network

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.
Improving Knowledge Graph Embedding Using Simple Constraints

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at https://github.com/i ieir-km/ComplEx-NNE_AER.
A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to "artificial language", such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.
Automatic question-answering using a deep similarity neural network

Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score. We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data. We have also tested our framework on a public question-answering database and achieved very good performance.
Insider Attack and Cyber Security - Beyond the Hacker

Where you can find the insider attack and cyber security beyond the hacker easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this insider attack and cyber security beyond the hacker book. It is about this book that will give wellness for all people from many societies.
Clinical practice. Graves' disease.

A 23-year-old woman presents with palpitations. Over the past 6 months, she has reported loose stools, a 10-lb (4.5-kg) weight loss despite a good appetite and food intake, and increased irritability. She appears to be anxious and has a pulse of 119 beats per minute and a blood pressure of 137/80 mm Hg. Her thyroid gland is diffusely and symmetrically enlarged to twice the normal size, and it is firm and nontender; a thyroid bruit is audible. She has an eyelid lag, but no proptosis or periorbital edema. The serum thyrotropin level is 0.02 μU per milliliter (normal range, 0.35 to 4.50) and the level of free thyroxine is 4.10 ng per deciliter (normal range, 0.89 to 1.76). How should she be further evaluated and treated?
Design and Evaluation of Hardware Pseudo-Random Number Generator MT19937

MT19937 is a kind of Mersenne Twister, which is a pseudo-random number generator. This study presents new designs for a MT19937 circuit suitable for custom computing machinery for highperformance scientific simulations. Our designs can generate multiple random numbers per cycle (multi-port design). The estimated throughput of a 52-port design was 262 Gbps, which is 115 times higher than the software on a Pentium 4 (2.53 GHz) processor. Multi-port designs were proven to be more cost-effective than using multiple single-port designs. The initialization circuit can be included without performance loss in exchange for a slight increase of logic scale. key words: custom circuit, simulation, random number, Mersenne Twister, FPGA
Deep learning with long short-term memory networks for financial market predictions

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015. With daily returns of 0.46 percent and a Sharpe Ratio of 5.8 prior to transaction costs, we find LSTM networks to outperform memoryfree classification methods, i.e., a random forest (RAF), a deep neural net (DNN), and a logistic regression classifier (LOG). We unveil sources of profitability, thereby shedding light into the black box of artificial neural networks. Specifically, we find one common pattern among the stocks selected for trading they exhibit high volatility and a short-term reversal return profile. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that is able to explain a portion of the returns of the LSTM.
The Psychology of Fraud

http://www.aic.gov.au Adam Graycar Director Fraud, like other crime, can best be explained by three factors: a supply of motivated offenders, the availability of suitable targets and the absence of capable guardians—control systems or someone “to mind the store”, so to speak (Cohen & Felson 1979). In this, the first of two papers, the authors focus on motivation and other psychological aspects of fraud. They identify a number of psychological correlates of fraud offending, but note that these are by no means unique to fraud, and do not necessarily differentiate fraudsters from law-abiding citizens. The other two factors, opportunities and guardianship, provide more scope for fraud control and will be addressed in a companion paper on “red flags”, or situational indicia, of fraud risk.
Agent Samples Network Topology Agent Learns From Memory

At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using Qlearning with an -greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks.
High self-control predicts good adjustment, less pathology, better grades, and interpersonal success.

What good is self-control? We incorporated a new measure of individual differences in self-control into two large investigations of a broad spectrum of behaviors. The new scale showed good internal consistency and retest reliability. Higher scores on self-control correlated with a higher grade point average, better adjustment (fewer reports of psychopathology, higher self-esteem), less binge eating and alcohol abuse, better relationships and interpersonal skills, secure attachment, and more optimal emotional responses. Tests for curvilinearity failed to indicate any drawbacks of so-called overcontrol, and the positive effects remained after controlling for social desirability. Low self-control is thus a significant risk factor for a broad range of personal and interpersonal problems.