nils-herrmann/clean_scirepeval huggingface dataset:
Convolutional Models for Joint Object Categorization and Pose Estimation

In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose regression using these approaches has received relatively much less attention. In this paper we show how deep architectures, specifically Convolutional Neural Networks (CNN), can be adapted to the task of simultaneous categorization and pose estimation of objects. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations of CNNs represent object pose information and how this contradicts object category representations. We extensively experiment on two recent large and challenging multi-view datasets. Our models achieve better than state-of-the-art performance on both datasets.
Food and Mood: Just-in-Time Support for Emotional Eating

Behavior modification in health is difficult, as habitual behaviors are extremely well-learned, by definition. This research is focused on building a persuasive system for behavior modification around emotional eating. In this paper, we make strides towards building a just-in-time support system for emotional eating in three user studies. The first two studies involved participants using a custom mobile phone application for tracking emotions, food, and receiving interventions. We found lots of individual differences in emotional eating behaviors and that most participants wanted personalized interventions, rather than a pre-determined intervention. Finally, we also designed a novel, wearable sensor system for detecting emotions using a machine learning approach. This system consisted of physiological sensors which were placed into women's brassieres. We tested the sensing system and found positive results for emotion detection in this mobile, wearable system.
Eye Blink Detection Using Variance of Motion Vectors

A new eye blink detection algorithm is proposed. It is based on analyzing the variance of the vertical component of motion vectors in the eye region. Face and eyes are detected with Viola – Jones type algorithm. Next, a grid of points is placed over the eye regions and tracked with a KLT tracker. Eye regions are divided into 3×3 cells. For each cell an average motion vector is estimated from motion vectors of the individual tracked points. Simple state machines are setup to analyse these variances for each eye. The solution is this way more robust and with a lower false positive rate compared to other methods based on tracking. We achieve the best results on the Talking face dataset (mean accuracy 99%) and state-of-the-art results on the ZJU dataset.
Multimodal Memory Modelling for Video Captioning

Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, video captioning has made great progress. However, learning an effective mapping from the visual sequence space to the language space is still a challenging problem due to the long-term multimodal dependency modelling and semantic misalignment. Inspired by the facts that memory modelling poses potential advantages to longterm sequential problems [35] and working memory is the key factor of visual attention , we propose a Multimodal Memory Model (M) to describe videos, which builds a visual and textual shared memory to model the longterm visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments. Specifically, similar to , the proposed M attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. To evaluate the proposed model, we perform experiments on two public datasets: MSVD and MSR-VTT. The experimental results demonstrate that our method outperforms most of the stateof-the-art methods in terms of BLEU and METEOR.
Predicting Depression Levels Using Social Media Posts

The use of Social Network Sites (SNS) is increasing nowadays especially by the younger generations. The availability of SNS allows users to express their interests, feelings and share daily routine. Many researchers prove that using user-generated content (UGC) in a correct way may help determine people's mental health levels. Mining the UGC could help to predict the mental health levels and depression. Depression is a serious medical illness, which interferes most with the ability to work, study, eat, sleep and having fun. However, from the user profile in SNS, we can collect all the information that relates to person's mood, and negativism. In this research, our aim is to investigate how SNS user's posts can help classify users according to mental health levels. We propose a system that uses SNS as a source of data and screening tool to classify the user using artificial intelligence according to the UGC on SNS. We created a model that classify the UGC using two different classifiers: Support Vector Machine (SVM), and Naïve Bayes.
Image Colorization with Deep Convolutional Neural Networks

We present a convolutional-neural-network-based system that faithfully colorizes black and white photographic images without direct human assistance. We explore various network architectures, objectives, color spaces, and problem formulations. The final classification-based model we build generates colorized images that are significantly more aesthetically-pleasing than those created by the baseline regression-based model, demonstrating the viability of our methodology and revealing promising avenues for future work.
BOAT adaptive credit card fraud detection system

Fraud is increasing with the extensive use of internet and the increase of online transactions. More advanced solutions are desired to protect financial service companies and credit card holders from constantly evolving online fraud attacks. The main objective of this paper is to construct an efficient fraud detection system which is adaptive to the behavior changes by combining classification and clustering techniques. This is a two stage fraud detection system which compares the incoming transaction against the transaction history to identify the anomaly using BOAT algorithm in the first stage. In second stage to reduce the false alarm rate suspected anomalies are checked with the fraud history database and make sure that the detected anomalies are due to fraudulent transaction or any short term change in spending profile. In this work BOAT supports incremental update of transactional database and it handles maximum fraud coverage with high speed and less cost. Proposed model is evaluated on both synthetically generated and real life data and shows very good accuracy in detecting fraud transaction.
Social anxiety and technology: Face-to-face communication versus technological communication among teens

This study examined teens’ use of socially interactive technologies (SITs), such as online social sites, cell phones/text messaging, and instant messaging (IM), and the role that social anxiety plays on how teens communicate with others (technologically or face-to-face). Participants included 280 high school students from a large western city. On average, 35–40% of teens reported using cell phones/text messaging and online social sites between 1 and 4 h daily, 24% reported using IMs 1–4 h daily and only 8% reported using email between 1 and 4 h daily. Females tended to use cell phones/text messaging and online social sites more so than did males. In assessing social anxiety, analyses revealed a positive relationship between social anxiety (not comfortable talking with others face-to-face) and (1) talking with others online and (2) talking with others via text messaging. In contrast, there was a positive relationship between the lack of social anxiety (feeling ‘‘comfortable” talking with others) and making friends online. Assessing gender differences and social anxiety also revealed significant differences. Results revealed females reported more social anxiety (not comfortable talking with others in person) than did males. In addition, females, more than males, reported feeling more comfortable using SITs (text messaging and online social sites only) rather than talking with others face-to-face.
Outlier Detection Using Nonconvex Penalized Regression

This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L1 penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The L1 penalty corresponds to soft thresholding. We introduce a thresholding (denoted by Θ) based iterative procedure for outlier detection (Θ-IPOD). A version based on hard thresholding correctly identifies outliers on some hard test problems. We find that Θ-IPOD is much faster than iteratively reweighted least squares for large data because each iteration costs at most O(np) (and sometimes much less) avoiding an O(np2) least squares estimate. We describe the connection between Θ-IPOD andM -estimators. Our proposed method has one tuning parameter with which to both identify outliers and estimate regression coefficients. A data-dependent choice can be made based on BIC. The tuned Θ-IPOD shows outstanding performance in identifying outliers in various situations in comparison to other existing approaches. This methodology extends to highdimensional modeling with p ≫ n, if both the coefficient vector and the outlier pattern are sparse.
Improving Lexical Choice in Neural Machine Translation

We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a simple lexical module which is jointly trained with the rest of the model. We evaluate our approaches on eight language pairs with data sizes ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU, surpassing phrasebased translation in nearly all
Smart Cities Data Streams Integration: experimenting with Internet of Things and social data flows

Smart cities are nowadays expanding and flourishing worldwide with Internet of Things (IoT), i.e. smart things like sensors and actuators, and mobile devices applications and installations which change the citizens' and authorities' everyday life. Smart cities produce daily huge streams of sensors data while citizens interact with Web and/or mobile devices utilizing social networks. In such a smart city context, new approaches to integrate big data streams from both sensors and social networks are needed to exploit big data production and circulation towards offering innovative solutions and applications. The SmartSantander infrastructure (EU FP7 project) has offered the ground for the SEN2SOC experiment which has integrated sensor and social data streams. This presentation outlines its research and industrial perspective and potential impact.
Auto-patching DOM-based XSS at scale

DOM-based cross-site scripting (XSS) is a client-side code injection vulnerability that results from unsafe dynamic code generation in JavaScript applications, and has few known practical defenses. We study dynamic code evaluation practices on nearly a quarter million URLs crawled starting from the the Alexa Top 1000 websites. Of 777,082 cases of dynamic HTML/JS code generation we observe, 13.3% use unsafe string interpolation for dynamic code generation — a well-known dangerous coding practice. To remedy this, we propose a technique to generate secure patches that replace unsafe string interpolation with safer code that utilizes programmatic DOM construction techniques. Our system transparently auto-patches the vulnerable site while incurring only 5.2 − 8.07% overhead. The patching mechanism requires no access to server-side code or modification to browsers, and thus is practical as a turnkey defense.
SAR ADC architecture with 98 % reduction in switching energy over conventional scheme

A high energy-efficiency switching scheme for a successive approximation register (SAR) analogue-to-digital converter (ADC) is presented. The proposed method can achieve 98.4% savings in switching energy when compared to a conventional SAR. The proposed technique also achieves a 4 × reduction in total capacitance used in the digital-to-analogue converter (DAC) compared to the conventional DAC.
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies

First, the covariance matrix adaptation (CMA) with rank-one update is introduced into the (1+1)-evolution strategy. An improved implementation of the 1/5-th success rule is proposed for step size adaptation, which replaces cumulative path length control. Second, an incremental Cholesky update for the covariance matrix is developed replacing the computational demanding and numerically involved decomposition of the covariance matrix. The Cholesky update can replace the decomposition only for the update without evolution path and reduces the computational effort from O(n3) to O(n2). The resulting (1+1)-Cholesky-CMA-ES is an elegant algorithm and the perhaps simplest evolution strategy with covariance matrix and step size adaptation. Simulations compare the introduced algorithms to previously published CMA versions.
Stakeholder marketing : a definition and conceptual framework

Stakeholder marketing has established foundational support for redefining and broadening the marketing discipline. An extensive literature review of 58 marketing articles that address six primary stakeholder groups (i.e., customers, suppliers, employees, shareholders, regulators, and the local community) provides evidence of the important role the groups play in stakeholder marketing. Based on this review and in conjunction with established marketing theory, we define stakeholder marketing as “activities and processes within a system of social institutions that facilitate and maintain value through exchange relationships with multiple stakeholders.” In an effort to focus on the stakeholder marketing field of study, we offer both a conceptual framework for understanding the pivotal role of stakeholder marketing and research questions for examining the linkages among stakeholder exchanges, value creation, and marketing outcomes.
Predicting equity market price impact with performance weighted ensembles of random forests

For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system's performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.
A Brief Introduction to Weakly Supervised Learning

Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of data labeling process. Thus, it is desired for machine learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision where only a subset of training data are given with labels; inexact supervision where the training data are given with only coarse-grained labels; inaccurate supervision where the given labels are not always ground-truth.
Assisted querying using instant-response interfaces

We demonstrate a novel query interface that enables users to construct a rich search query without any prior knowledge of the underlying schema or data. The interface, which is in the form of a single text input box, interacts in real-time with the users as they type, guiding them through the query construction. We discuss the issues of schema and data complexity, result size estimation, and query validity; and provide novel approaches to solving these problems. We demonstrate our query interface on two popular applications; an enterprise-wide personnel search, and a biological information database.
Data Mining - Concepts, Models and Techniques

Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.
AuDroid: Preventing Attacks on Audio Channels in Mobile Devices

Voice control is a popular way to operate mobile devices, enabling users to communicate requests to their devices. However, adversaries can leverage voice control to trick mobile devices into executing commands to leak secrets or to modify critical information. Contemporary mobile operating systems fail to prevent such attacks because they do not control access to the speaker at all and fail to control when untrusted apps may use the microphone, enabling authorized apps to create exploitable communication channels. In this paper, we propose a security mechanism that tracks the creation of audio communication channels explicitly and controls the information flows over these channels to prevent several types of attacks. We design and implement AuDroid, an extension to the SE Linux reference monitor integrated into the Android operating system for enforcing lattice security policies over the dynamically changing use of system audio resources. To enhance flexibility, when information flow errors are detected, the device owner, system apps and services are given the opportunity to resolve information flow errors using known methods, enabling AuDroid to run many configurations safely. We evaluate our approach on 17 widely-used apps that make extensive use of the microphone and speaker, finding that AuDroid prevents six types of attack scenarios on audio channels while permitting all 17 apps to run effectively. AuDroid shows that it is possible to prevent attacks using audio channels without compromising functionality or introducing significant performance overhead.
Fingerprint-Based Recognition

Biometric recognition, or biometrics, refers to the authentication of an individual based on her/his biometric traits. Among the various biometric traits (e.g., face, iris, fingerprint, voice), fingerprint-based authentication has the longest history, and has been successfully adopted in both forensic and civilian applications. Advances in fingerprint capture technology have resulted in new large scale civilian applications (e.g., US-VISIT program). However, these systems still encounter difficulties due to various noise factors present in operating environments. The purpose of this article is to give an overview of fingerprint-based recognition and discuss research opportunities for making these systems perform more effectively.
Single bit error correction implementation in CRC-16 on FPGA

Framing protocols employ cyclic redundancy check (CRC) to detect errors incurred during transmission. Generally whole frame is protected using CRC and upon detection of error, retransmission is requested. But certain protocols demand for single bit error correction capabilities for the header part of the frame, which often plays an important role in receiver synchronization. At a speed of 10 Gbps, header error correction implementation in hardware can be a bottleneck. This work presents a hardware efficient way of implementing CRC-16 over 16 bits of data, multiple bit error detection and single bit error correction on FPGA device.
Fast Databases with Fast Durability and Recovery Through Multicore Parallelism

Multicore in-memory databases for modern machines can support extraordinarily high transaction rates for online transaction processing workloads. A potential weakness, however, is recovery from crash failures. Can classical techniques, such as checkpoints, be made both efficient enough to keep up with current systems’ memory sizes and transaction rates, and smart enough to avoid additional contention? Starting from an efficient multicore database system, we show that naive logging and checkpoints make normal-case execution slower, but that frequent disk synchronization allows us to keep up with many workloads with only a modest reduction in throughput. We design throughout for parallelism: during logging, during checkpointing, and during recovery. The result is fast. Given appropriate hardware (three SSDs and a RAID), a 32-core system can recover a 43.2 GB key-value database in 106 seconds, and a > 70 GB TPC-C database in 211 seconds.
The ForSpec Temporal Logic: A New Temporal Property-Specification Language

In this paper we describe the ForSpec Temporal Logic (FTL), the new temporal property-specification logic of ForSpec, Intel’s new formal specification language. The key features of FTL are as follows: it is a l inear temporal logic, based on Pnueli’s LTL, it is based on a rich set of logic al and arithmetical operations on bit vectors to describe state properties, it enables the user to define temporal connectives over time windows, it enables th user to define regular events, which are regular sequences of Boolean events, and then relate such events via special connectives, it enables the user to expre ss roperties about the past, and it includes constructs that enable the user to mode l multiple clock and reset signals, which is useful in the verification of hardwar e design.
Kangaroo Vehicle Collision Detection Using Deep Semantic Segmentation Convolutional Neural Network

Kangaroo vehicle collisions are a serious problem threatening the safety of the drivers on Australian roads. It is estimated, according to a recent report by Australian Associated Motor Insurers, that there are around 20,000 kangaroo vehicle collisions during year 2015 in Australia. As a result, more than AU $75 million in insurance claims, and a number of animal and human severe injuries and fatalities have been reported. Despite how catastrophic these numbers are, yet a little research has been done in order to avoid or minimise the number of kangaroo vehicle collisions. In this work, we are focusing on the problem of recognising and detecting kangaroos in dynamic environments using a deep semantic segmentation convolutional neural network model. Our model is trained on a synthetic labelled depth images obtained using a simulated range sensor. Our approach records average recall value of over 93% in semantically segmenting any number of kangaroos in the generated testing dataset.