nils-herrmann/clean_scirepeval huggingface dataset:
Multi-label Cross-Modal Retrieval

In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations. In particular, we introduce multi-label Canonical Correlation Analysis (ml-CCA), an extension of CCA, for learning shared subspaces taking into account high level semantic information in the form of multi-label annotations. Unlike CCA, ml-CCA does not rely on explicit pairing between modalities, instead it uses the multi-label information to establish correspondences. This results in a discriminative subspace which is better suited for cross-modal retrieval tasks. We also present Fast ml-CCA, a computationally efficient version of ml-CCA, which is able to handle large scale datasets. We show the efficacy of our approach by conducting extensive cross-modal retrieval experiments on three standard benchmark datasets. The results show that the proposed approach achieves state of the art retrieval performance on the three datasets.
Constructing the ScratchJr programming language in the early childhood classroom

This paper seeks to contribute to the growing literature on children and computer programming by focusing on a programming language for children in Kindergarten through second grade. Sixty-two students were exposed to a 6-week curriculum using ScartchJr. They learned foundational programming concepts and applied those concepts to create personally meaningful projects using the ScratchJr programming app. This paper addresses the following research question: Which ScratchJr programming blocks do young children choose to use in their own projects after they have learned them all through a tailored programming curriculum? Data was collected in the form of the students’ combined 977 projects, and analyzed for patterns and differences across grades. This paper summarizes findings and suggests potential directions for future research. Implications for the use of ScratchJr as an introductory programming language for young children are also discussed.
Visual Question Answering: A Survey of Methods and Datasets

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.
A Delay-Locked Loop Synchronization Scheme for High-Frequency Multiphase Hysteretic DC-DC Converters

This paper reports a delay-locked loop (DLL) based hysteretic controller for high-frequency multiphase dc-dc buck converters. The DLL control loop employs the switching frequency of a hysteretic comparator as reference to automatically synchronize the remaining phases and eliminate the need for external synchronization. A dedicated duty cycle control loop is used to enable current sharing and ripple cancellation. We demonstrate a four-phase high-frequency buck converter that operates at 25-70 MHz with fast hysteretic control and output conversion range of 17.5%-80%. The converter achieves an efficiency of 83% at 2 W and 80% at 3.3 W. The circuit has been implemented in standard 0.5 mum 5 V CMOS process.
Word sense and semantic relations in noun compounds

In this article, we investigate word sense distributions in noun compounds (NCs). Our primary goal is to disambiguate the word sense of component words in NCs, based on investigation of “semantic collocation” between them. We use sense collocation and lexical substitution to build supervised and unsupervised word sense disambiguation (WSD) classifiers, and show our unsupervised learner to be superior to a benchmark WSD system. Further, we develop a word sense-based approach to interpreting the semantic relations in NCs.
A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution

Super-resolution algorithms reconstruct a high resolution image from a set of low resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low resolution images are undersampled and have aliasing artifacts, the performance of standard registration algorithms decreases. We propose a frequency domain technique to precisely register a set of aliased images, based on their low-frequency, aliasing-free part. A high resolution image is then reconstructed using cubic interpolation. Our algorithm is compared to other algorithms in simulations and practical experiments using real aliased images. Both show very good visual results and prove the attractivity of our approach in the case of aliased input images. A possible application is to digital cameras where a set of rapidly acquired images can be used to recover a higher resolution final image. Index Terms Super-resolution imaging, image registration, aliasing
Macroeconomics and FinTech: Uncovering Latent Macroeconomic Effects on Peer-to-Peer Lending

Peer-to-peer (P2P) lending is a fast growing financial technology (FinTech) trend that is displacing traditional retail banking. Studies on P2P lending have focused on predicting individual interest rates or default probabilities. However, the relationship between aggregated P2P interest rates and the general economy will be of interest to investors and borrowers as the P2P credit market matures. We show that the variation in P2P interest rates across grade types are determined by three macroeconomic latent factors formed by Canonical Correlation Analysis (CCA) — macro default, investor uncertainty, and the fundamental value of the market. However, the variation in P2P interest rates across term types cannot be explained by the general economy.
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks

We present a stochastic first-order optimization algorithm, named BCSC, that adds a cyclic constraint to stochastic block-coordinate descent. It uses different subsets of the data to update different subsets of the parameters, thus limiting the detrimental effect of outliers in the training set. Empirical tests in benchmark datasets show that our algorithm outperforms state-of-the-art optimization methods in both accuracy as well as convergence speed. The improvements are consistent across different architectures, and can be combined with other training techniques and regularization methods.
Enabling Fine-Grained Edge Offloading for IoT

In this paper we make the case for IoT edge offloading, which strives to exploit the resources on edge computing devices by offloading fine-grained computation tasks from the cloud closer to the users and data generators (i.e., IoT devices). The key motive is to enhance performance, security and privacy for IoT services. Our proposal bridges the gap between cloud computing and IoT by applying a divide and conquer approach over the multi-level (cloud, edge and IoT) information pipeline. To validate the design of IoT edge offloading, we developed a unikernel-based prototype and evaluated the system under various hardware and network conditions. Our experimentation has shown promising results and revealed the limitation of existing IoT hardware and virtualization platforms, shedding light on future research of edge computing and IoT.
Effect of protein/essential amino acids and resistance training on skeletal muscle hypertrophy: A case for whey protein

Regardless of age or gender, resistance training or provision of adequate amounts of dietary protein (PRO) or essential amino acids (EAA) can increase muscle protein synthesis (MPS) in healthy adults. Combined PRO or EAA ingestion proximal to resistance training, however, can augment the post-exercise MPS response and has been shown to elicit a greater anabolic effect than exercise plus carbohydrate. Unfortunately, chronic/adaptive response data comparing the effects of different protein sources is limited. A growing body of evidence does, however, suggest that dairy PRO, and whey in particular may: 1) stimulate the greatest rise in MPS, 2) result in greater muscle cross-sectional area when combined with chronic resistance training, and 3) at least in younger individuals, enhance exercise recovery. Therefore, this review will focus on whey protein supplementation and its effects on skeletal muscle mass when combined with heavy resistance training.
RFID Tag and RF Structures on a Paper Substrate Using Inkjet-Printing Technology

In this paper, inkjet-printed UHF and microwave circuits fabricated on paper substrates are investigated for the first time as an approach that aims for a system-level solution for fast and ultra-low-cost mass production. First, the RF characteristics of the paper substrate are studied by using the microstrip ring resonator in order to characterize the relative permittivity (epsivr) and loss tangent (tan delta) of the substrate at the UHF band for the first time reported. A UHF RFID tag module is then developed with the inkjet-printing technology, proving this approach could function as an enabling technology for much simpler and faster fabrication on/in paper. Simulation and well-agreed measurement results, which show very good agreement, verify a good performance of the tag module. In addition, the possibility of multilayer RF structures on a paper substrate is explored, and a multilayer patch resonator bandpass filter demonstrates the feasibility of ultra-low-cost 3-D paper-on-paper RF/wireless structures.
The Financial Crimes Enforcement Network AI System (FAIS) Identifying Potential Money Laundering from Reports of Large Cash Transactions

CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (that functions as a blackboard), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed. FAIS’s unique analytic power arises primarily from a change in view of the underlying data from a transaction-oriented perspective to a subject-oriented (that is, person or organization) perspective. The Financial Crimes Enforcement Network (FINCEN) is a relatively new agency (founded in 1990) of the U.S. Treasury Department whose mission is to establish, oversee, and implement policies to prevent and detect money laundering in support of federal, state, and local law enforcement. A key data source available to FINCEN is reports of large cash transactions made to the Treasury according to terms of the Bank Secrecy Act.2 FINCEN has developed a system, called the FINCEN AI System (FAIS), which links and evaluates all reported transactions for indications of suspicious activity characteristic of money laundering, with the objective of identifying previously unknown, potentially highvalue leads for follow-up investigation and, if warranted, prosecution (Wall Street Journal 1993). FAIS integrates intelligent software and human agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database, a graphic user interface, and several preprocessing and analysis modules. The database functions as a blackboard and is implemented in SYBASE. The graphic user interface is implemented in Neuron Data’s OPEN INTERFACE. The suspiciousness evaluation module is a rule-based reasoner implemented in Neuron Data’s NEXPERT OBJECT (now called Articles
Direct Active and Reactive Power Regulation of DFIG Using Sliding-Mode Control Approach

This paper presents a new direct active and reactive power control (DPC) of grid-connected doubly fed induction generator (DFIG)-based wind turbine systems. The proposed DPC strategy employs a nonlinear sliding-mode control scheme to directly calculate the required rotor control voltage so as to eliminate the instantaneous errors of active and reactive powers without involving any synchronous coordinate transformations. Thus, no extra current control loops are required, thereby simplifying the system design and enhancing the transient performance. Constant converter switching frequency is achieved by using space vector modulation, which eases the designs of the power converter and the ac harmonic filter. Simulation results on a 2-MW grid-connected DFIG system are provided and compared with those of classic voltage-oriented vector control (VC) and conventional lookup table (LUT) DPC. The proposed DPC provides enhanced transient performance similar to the LUT DPC and keeps the steady-state harmonic spectra at the same level as the VC strategy.
DAMNED IF YOU DON ’ T , DAMNED IF YOU DO : CRIME MAPPING AND ITS IMPLICATIONS IN THE REAL WORLD

A small but growing North American trend is the publication of maps of crime on the Internet. A number of web sites allow observers to view the spatial distribution of crime in various American cities, often to a considerable resolution, and increasingly in an interactive format. The use of Geographical Information Systems (GIS) technology to map crime is a rapidly expanding field that is, as this paper will explain, still in a developmental stage, and a number of technical and ethical issues remain to be resolved. The public right to information about local crime has to be balanced by a respect for the privacy of crime victims. Various techniques are being developed to assist crime mappers to aggregate spatial data, both to make their product easier to comprehend and to protect identification of the addresses of crime victims. These data aggregation techniques, while preventing identification of individuals, may also be inadvertently producing maps with the appearance of ‘greater risk’ in low crime areas. When some types of crime mapping have the potential to cause falling house prices, increasing insurance premiums or business abandonment, conflicts may exist between providing a public service and protecting the individual, leaving the cartographer vulnerable to litigation.
Transformational Sparse Coding

A fundamental problem faced by object recognition systems is that objects and their features can appear in different locations, scales and orientations. Current deep learning methods attempt to achieve invariance to local translations via pooling, discarding the locations of features in the process. Other approaches explicitly learn transformed versions of the same feature, leading to representations that quickly explode in size. Instead of discarding the rich and useful information about feature transformations to achieve invariance, we argue that models should learn object features conjointly with their transformations to achieve equivariance. We propose a new model of unsupervised learning based on sparse coding that can learn object features jointly with their affine transformations directly from images. Results based on learning from natural images indicate that our approach matches the reconstruction quality of traditional sparse coding but with significantly fewer degrees of freedom while simultaneously learning transformations from data. These results open the door to scaling up unsupervised learning to allow deep feature+transformation learning in a manner consistent with the ventral+dorsal stream architecture of the primate visual cortex.
The Need for Effective Information Security Awareness

Security awareness is an often-overlooked factor in an information security program. While organizations expand their use of advanced security technology and continuously train their security professionals, very little is used to increase the security awareness among the normal users, making them the weakest link in any organization. As a result, today, organized cyber criminals are putting significant efforts to research and develop advanced hacking methods that can be used to steal money and information from the general public. Furthermore, the high internet penetration growth rate in the Middle East and the limited security awareness among users is making it an attractive target for cyber criminals. In this paper, we will show the need for security awareness programs in schools, universities, governments, and private organizations in the Middle East by presenting results of several security awareness studies conducted among students and professionals in UAE in 2010. This includes a comprehensive wireless security survey in which thousands of access points were detected in Dubai and Sharjah most of which are either unprotected or employ weak types of protection. Another study focuses on evaluating the chances of general users to fall victims to phishing attacks which can be used to steal bank and personal information. Furthermore, a study of the user’s awareness of privacy issues when using RFID technology is presented. Finally, we discuss several key factors that are necessary to develop a successful information security awareness program.
Alphabet Soup Blurring the Distinctions Between p ’ s and a ’ s in Psychological Research

Confusion over the reporting and interpretation of results of commonly employed classical statistical tests is recorded in a sample of 1,645 papers from 12 psychology journals for the period 1990 through 2002. The confusion arises because researchers mistakenly believe that their interpretation is guided by a single unified theory of statistical inference. But this is not so: classical statistical testing is a nameless amalgamation of the rival and often contradictory approaches developed by Ronald Fisher, on the one hand, and Jerzy Neyman and Egon Pearson, on the other. In particular, there is extensive failure to acknowledge the incompatibility of Fisher’s evidential p value with the Type I error rate, α, of Neyman–Pearson statistical orthodoxy. The distinction between evidence (p’s) and errors (α’s) is not trivial. Rather, it reveals the basic differences underlying Fisher’s ideas on significance testing and inductive inference, and Neyman–Pearson views on hypothesis testing and inductive behavior. So complete is this misunderstanding over measures of evidence versus error that it is not viewed as even being a problem among the vast majority of researchers and other relevant parties. These include the APA Task Force on Statistical Inference, and those writing the guidelines concerning statistical testing mandated in APA Publication Manuals. The result is that, despite supplanting Fisher’s significance-testing paradigm some fifty years or so ago, recognizable applications of Neyman–Pearson theory are few and far between in psychology’s empirical literature. On the other hand, Fisher’s influence is ubiquitous.
Active learning based news veracity detection with feature weighting and deep-shallow fusion

The objective of a news veracity detection system is to identify various types of potentially misleading or false information, typically in a digital platform. A critical challenge in this scenario is that there are large volumes of data available online. However, obtaining samples with annotations (i.e. ground-truth labels) is difficult and a known limiting factor for many data analytic tasks including the current problem of news veracity detection. In this paper, we propose a human-machine collaborative learning system to evaluate the veracity of a news content, with a limited amount of annotated data samples. In a semi-supervised scenario, an initial classifier is learnt on a small, limited amount of the annotated data followed by an interactive approach to gradually update the model by shortlisting only relevant samples from the large pool of unlabeled data that are most likely to improve the classifier performance. Our prioritized active learning solution achieves faster convergence in terms of the classification performance, while requiring about 1–2 orders of magnitude fewer annotated samples compared to fully supervised solutions to attain a reasonably acceptable accuracy of nearly 80%. Unlike traditional deep learning architecture, the proposed active learning based deep model designed with a smaller number of more localized filters per layer can efficiently learn from small relevant sample batches that can effectively improve performance in the weakly-supervised learning environment and thus is more suitable for several practical applications. An effective dynamic domain adaptive feature weighting scheme can adjust the relative importance of feature dimensions iteratively. Insightful initial feedback gathered from two independent learning modules (a NLP shallow feature based classifier and a deep classifier), modeled to capture complementary information about data characteristics are finally fused together to achieve an impressive 25% average gain in the detection performance.
Analyzing Worst-Case Delay Performance of IEC 61850-9-2 Process Bus Networks Using Measurements and Network Calculus

In power substation automation systems (SASs) based on IEC 61850, conventional hardwired process connections are being replaced by switched Ethernet. To ensure system reliability and responsiveness, transmission of critical information required by protection and control tasks must satisfy hard delay constraints at all times. Therefore, delay performance conformance should be taken into consideration during the design phase of an SAS project. In this paper, we propose to study the worst-case delay performance of IEC 61850-9-2 process bus networks, which generally carry non-feedforward traffic patterns, through the combination of measurements and network-calculus-based analysis. As an Ethernet switch supports dedicated interconnections between its multiple interfaces, our proposed approach converts a non-feedforward network into feedforward ones by introducing service models for its individual output interfaces instead of modeling it in its entirety with a single service model. To derive practical delay bounds that can be validated against measurement results, our approach not only constructs traffic models based on the idiosyncrasies of process bus network and switched Ethernet, but also establishes service models of networking devices by taking measurements. Results from our case studies of both feedforward and non-feedforward process bus networks show that the proposed combination of network calculus and measurement-based modeling generates accurate delay bounds for Ethernet-based substation communication networks (SCNs). The proposed approach can thus be adopted by designers and architects to analytically evaluate worst-case delay performance at miscellaneous stages of SAS design.