nils-herrmann/clean_scirepeval huggingface dataset:
A Practical Evaluation of Kinetic Data Structures

In many applications of computational geometry to modeling objects and processes in the physical world, the participating objects are in a state of continuous change. Motion is the most ubiquitous kind of continuous transformation but others, such as shape deformation, are also possible. In a recent paper, Baech, Guibas, and Hershberger [BGH97] proposed the framework of kinetic data structures (KDSS) as a way to maintain, in a completely on-line fashion, desirable information about the state of a geometric system in continuous motion or change. They gave examples of kinetic data structures for the maximum of a set of (changing) numbers, and for the convex hull and closest pair of a set of (moving) points in the plane. The KDS frameworkallowseach object to change its motion at will according to interactions with other moving objects, the environment, etc. We implemented the KDSSdescribed in [BGH97],es well as came alternative methods serving the same purpose, as a way to validate the kinetic data structures framework in practice. In this note, we report some preliminary results on the maintenance of the convex hull, describe the experimental setup, compare three alternative methods, discuss the value of the measures of quality for KDSS proposed by [BGH97],and highlight some important numerical issues.
Algorithms for graph partitioning on the planted partition model

The NP-hard graph bisection problem is to partition the nodes of an undirected graph into two equal-sized groups so as to minimize the number of edges that cross the partition. The more general graph l-partition problem is to partition the nodes of an undirected graph into l equal-sized groups so as to minimize the total number of edges that cross between groups. We present a simple, linear-time algorithm for the graph l-partition problem and analyze it on a random \planted l-partition" model. In this model, the n nodes of a graph are partitioned into l groups, each of size n=l; two nodes in the same group are connected by an edge with some probability p, and two nodes in diierent groups are connected by an edge with some probability r < p. We show that if p ? r n ?1=2+ for some constant , then the algorithm nds the optimal partition with probability 1 ? exp(?n ()).
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X , Z and Y wherein we are interested in generating sequences in Y starting from information available in X . However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y . Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encodeX and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.
The Quest to Automate Fact-checking

The growing movement of political fact-checking plays an important role in increasing democratic accountability and improving political discourse [7, 3]. Politicians and media figures make claims about “facts” all the time, but the new army of fact-checkers can often expose claims that are false, exaggerated or half-truths. The number of active fact-checking websites has grown from 44 a year ago to 64 this year, according the Duke Reporters’s Lab. 1 The challenge is that the human fact-checkers frequently have difficulty keeping up with the rapid spread of misinformation. Technology, social media and new forms of journalism have made it easier than ever to disseminate falsehoods and half-truths faster than the fact-checkers can expose them. There are several reasons that the falsehoods frequently outpace the truth. One reason is that fact-checking is an intellectually demanding and laborious process. It requires more research and a more advanced style of writing than ordinary journalism. The difficulty of fact-checking, exacerbated by a lack of resources for investigative journalism, leaves many harmful claims unchecked, particularly at the local level. Another reason is that fact-checking is time-consuming. It takes about one day to research and write a typical article, which means a lot of time can lapse after the political message. Even if the fact-check has already been published, the voter must undertake research to look it up. This “gap” in time and availability limits the effectiveness of fact-checking. Computation may hold the key to far more effective and efficient fact-checking, as Cohen et al. [1, 2] and Diakopolous 2 have pointed out. Over and over again, computing has reshaped journalism. Tasks that required huge amounts of manual labor such as analyzing data and finding patterns and relationships are now accomplished with ease.
An Android Communication App Forensic Taxonomy.

Due to the popularity of Android devices and applications (apps), Android forensics is one of the most studied topics within mobile forensics. Communication apps, such as instant messaging and Voice over IP (VoIP), are one popular app category used by mobile device users, including criminals. Therefore, a taxonomy outlining artifacts of forensic interest involving the use of Android communication apps will facilitate the timely collection and analysis of evidentiary materials from such apps. In this paper, 30 popular Android communication apps were examined, where a logical extraction of the Android phone images was collected using XRY, a widely used mobile forensic tool. Various information of forensic interest, such as contact lists and chronology of messages, was recovered. Based on the findings, a two-dimensional taxonomy of the forensic artifacts of the communication apps is proposed, with the app categories in one dimension and the classes of artifacts in the other dimension. Finally, the artifacts identified in the study of the 30 communication apps are summarized using the taxonomy. It is expected that the proposed taxonomy and the forensic findings in this paper will assist forensic investigations involving Android communication apps.
Consistent resting-state networks across healthy subjects.

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.
Inverse Reinforcement Learning from Failure

In this paper, we approach the problem of Inverse Reinforcement Learning (IRL) from a rather different perspective. Instead of trying to only mimic an expert as in traditional IRL, we present a method that can also utilise failed or bad demonstrations of a task. In particular, we propose a new IRL algorithm that extends the state-of-the-art method of Maximum Causal Entropy Inverse Reinforcement Learning to exploit such failed demonstrations. Furthermore, we present experimental results showing that our method can learn faster and better than its original counterpart.
Investigation on transformer design of high frequency high efficiency dc-dc converters

This paper studies the high-frequency high-efficiency transformer design. Several novel concepts are proposed to reveal the essence of the transformer design. In order to minimize the winding loss, several winding structure are proposed and compared. The planar transformer winding with PCB or spiral windings are discussed. The key factors to achieve the low winding losses are presented. The transformer design of the LLC resonant converter is investigated. The impacts on integration of magnetizing inductance and leakage inductance are studied.
3D integration with PCB technology

The future of 3D integration and packaging of power electronics using printed circuit board (PCB) technology is presented. This is to show how power electronics can benefit from the same advantages that have been exploited by the microelectronic industry, for some time already, regarding high density packaging, as implemented in modern digital photo and video cameras for example. Complementary technologies, enhancing the role of the printed circuit board from mere electrical interconnect medium to means to perform electromagnetic integration of passives, using specialised laminate material; to perform volumetric optimisation, using flexible foil; and to perform thermal management, using existing material and volume; are investigated as well as the specific material and technology limitations being faced by design engineers today. The investigation lead to a PCB assembled power converter implementing electromagnetic integration of passives, a flexible foil integrated-LCT winding realisation as well as a novel 3D-folding packaging method to obtain increased power density. The impact which the exploitation of these combined technologies introduce has been quantified using recently developed performance indicators, from which a critical look on aspects of improvement is possible. Experimental validation of the presented performance indicators are performed by means of a technology demonstrator. The design criteria and technology choices for this demonstrator, based on planar core-, in combination with flexible foil winding realisation technology, are also addressed.
MovieScope : Movie trailer classification using Deep Neural Networks

This paper deals with identifying the genre of a movie by analyzing just the visual features of its trailer. This task seems to be very trivial for a human; our endeavor is to create a vision system that can do the same, accurately. We discuss the approaches we take and our experimental observations. The contributions of this work are : (1) we propose a neural network (based on VGG) that can classify movie trailers based on their genres; (2) we release a curated dataset, called YouTube-Trailer Dataset, which has over 800 movie trailers spanning over 4 genres. We achieve an accuracy of 80.1% with the spatial features, and 85% with using LSTM and set these results as the benchmark for this dataset. We have made the source code publicly
Reputation and Impact in Academic Careers

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.
0.18μm 100V-rated BCD with large area power LDMOS with ultra-low effective specific resistance

In this paper, we present the integration of RESURF high-voltage lateral Power MOSFETs which achieve highly competitive figures of merit (such as Rsp, defined as Rdson*Area) in a Trench-Isolated 0.18 micron 100V-rated BCD technology. The new high-voltage LDMOS are rated for operation up to 45V, 60V, 80V and 100V and achieve BVdss & Rsp of 55V/32 mOhm*mm2, 75V/57 mOhm*mm2, 100V/91 mOhm*mm2 and 128V/206 mOhm*mm2 respectively. The HV devices are integrated with high-performance and fully isolated 8V to 30V LDMOS with low Rsp, 1.8V and 5V CMOS, as well as bipolar devices, diodes and other passive devices. The devices are isolated laterally using 20 micron deep trenches with air-gap, for low stress and minimized capacitance. Large 1mm2 Power MOSFETs were fabricated and characterized in order to determine the “effective Rsp“ including metallization, using a three metal back-end with 4um thick top aluminum metallization.
A Geolocation Databases Study

The geographical location of Internet IP addresses is important for academic research, commercial and homeland security applications. Thus, both commercial and academic databases and tools are available for mapping IP addresses to geographic locations. Evaluating the accuracy of these mapping services is complex since obtaining diverse large scale ground truth is very hard. In this work we evaluate mapping services using an algorithm that groups IP addresses to PoPs, based on structure and delay. This way we are able to group close to 100,000 IP addresses world wide into groups that are known to share a geo-location with high confidence. We provide insight into the strength and weaknesses of IP geolocation databases, and discuss their accuracy and encountered anomalies.
Automated Facial Expression Recognition Based on FACS Action Units

Automated recognition of facial expression is an important addition to computer vision research because of its relevance to the study of psychological phenomena and the development of human-computer interaction (HCI). We developed a computer vision system that automatically recognizes individual action units or action unit combinations in the upper face using Hidden Markov Models (HMMs). Our approach to facial expression recognition is based on the Facial Action Coding System (FACS), which separates expressions into upper and lower face action. In this paper, we use three approaches to extract facial expression information: (1) facial feature point tracking, (2) dense flow tracking with principal component analysis (PCA), and (3) high gradient component detection (i.e., furrow detection). The recognition results of the upper face expressions using feature point tracking, dense flow tracking, and high gradient component detection are 85%, 93%, and 85%,
Person Re-identification: System Design and Evaluation Overview

Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant variations across camera views, and there are a large number of pedestrians to be distinguished given small pedestrian images from surveillance videos. This chapter discusses different approaches of improving the key components of a person reidentification system, including feature design, feature learning and metric learning, as well as their strength and weakness. It provides an overview of various person reidentification systems and their evaluation on benchmark datasets. Mutliple benchmark datasets for person re-identification are summarized and discussed. The performance of some state-of-the-art person identification approaches on benchmark datasets is compared and analyzed. It also discusses a few future research directions on improving benchmark datasets, evaluation methodology and system desgin.
Simulation and Analysis of 3-D Magnetic Flux Leakage

In this paper, we present simulation results and analysis of 3-D magnetic flux leakage (MFL) signals due to the occurrence of a surface-breaking defect in a ferromagnetic specimen. The simulations and analysis are based on a magnetic dipole-based analytical model, presented in a previous paper. We exploit the tractability of the model and its amenability to simulation to analyze properties of the model as well as of the MFL fields it predicts, such as scale-invariance, effect of lift-off and defect shape, the utility of the tangential MFL component, and the sensitivity of MFL fields to parameters. The simulations and analysis show that the tangential MFL component is indeed a potentially critical part of MFL testing. It is also shown that the MFL field of a defect varies drastically with lift-off. We also exploit the model to develop a lift-off compensation technique which enables the prediction of the size of the defect for a range of lift-off values.
Machine Learning-based Sentiment Analysis of Automatic Indonesian Translations of English Movie Reviews

Sentiment analysis is the automatic classification of the overall opinion conveyed by a text towards its subject matter. This paper discusses an experiment in the sentiment analysis of of a collection of movie reviews that have been automatically translated to Indonesian. Following , we employ three well known classification techniques: naive bayes, maximum entropy, and support vector machines, employing unigram presence and frequency values as the features. The translation is achieved through machine translation and simple word substitutions based on a bilingual dictionary constructed from various online resources. Analysis of the Indonesian translations yielded an accuracy of up to 78.82%, still short of the accuracy for the English documents (80.09%), but satisfactorily high given the simple translation approach.
A Model Building Process for Identifying Actionable Static Analysis Alerts

Automated static analysis can identify potential source code anomalies early in the software process that could lead to field failures. However, only a small portion of static analysis alerts may be important to the developer (actionable). The remainder are false positives (unactionable). We propose a process for building false positive mitigation models to classify static analysis alerts as actionable or unactionable using machine learning techniques. For two open source projects, we identify sets of alert characteristics predictive of actionable and unactionable alerts out of 51 candidate characteristics. From these selected characteristics, we evaluate 15 machine learning algorithms, which build models to classify alerts. We were able to obtain 88-97% average accuracy for both projects in classifying alerts using three to 14 alert characteristics. Additionally, the set of selected alert characteristics and best models differed between the two projects, suggesting that false positive mitigation models should be project-specific.
MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.
Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns

Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we present novel features, termed dense center-symmetric local binary patterns (CS-LBP) and pyramid center-symmetric local binary/ternary patterns (CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al.  mainly captures the texture information. The proposed CS-LBP feature, in contrast, captures the gradient information and some texture information. Moreover, the proposed dense CS-LBP and the pyramid CS-LBP/LTP are easy to implement and computationally efficient, which is desirable for real-time applications. Experiments on the INRIA pedestrian dataset show that the dense CS-LBP feature with linear supporct vector machines (SVMs) is comparable with the histograms of oriented gradients (HOG) feature with linear SVMs, and the pyramid CS-LBP/LTP features outperform both HOG features with linear SVMs and the start-of-the-art pyramid HOG (PHOG) feature with the histogram intersection kernel SVMs. We also demonstrate that the combination of our pyramid CS-LBP feature and the PHOG feature could significantly improve the detection performance—producing state-of-the-art accuracy on the INRIA pedestrian dataset.