nils-herrmann/clean_scirepeval huggingface dataset:
EMD-L1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors

We propose a fast algorithm, EMD-L1, for computing the Earth Mover’s Distance (EMD) between a pair of histograms. Compared to the original formulation, EMD-L1 has a largely simplified structure. The number of unknown variables in EMD-L1 is O(N) that is significantly less than O(N) of the original EMD for a histogram with N bins. In addition, the number of constraints is reduced by half and the objective function is also simplified. We prove that the EMD-L1 is formally equivalent to the original EMD with L1 ground distance without approximation. Exploiting the L1 metric structure, an efficient tree-based algorithm is designed to solve the EMD-L1 computation. An empirical study demonstrates that the new algorithm has the time complexity of O(N), which is much faster than previously reported algorithms with super-cubic complexities. The proposed algorithm thus allows the EMD to be applied for comparing histogram-based features, which is practically impossible with previous algorithms. We conducted experiments for shape recognition and interest point matching. EMD-L1 is applied to compare shape contexts on the widely tested MPEG7 shape dataset and SIFT image descriptors on a set of images with large deformation, illumination change and heavy noise. The results show that our EMD-L1based solutions outperform previously reported state-of-the-art features and distance measures in solving the two tasks.
A PERSONALIZED WEB SEARCH BASED ON USER PROFILE AND USER CLICKS

Generally web search engines are built to serve all users, independent of the individual needs of any user. Personalization of web search is to carry out retrieval for each user incorporating their interest. This has become an important factor in daily usage as it improves the retrieval effectiveness on topics that the user would look for. There are several studies that have been done in this field. This paper proposes a new idea on personalized web search which is based on user profile and user clicks.
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.
Identifying Personality Traits , and Especially Traits Resulting in Violent Behavior through Automatic Handwriting Analysis

Handwriting analysis is a process that has been carried out for centuries. But its effectiveness when analyzing the behavior and personality of an individual is still a debate. Is it possible to detect potential deviant behavior and personality traits of an individual by carrying out an analysis of his/her handwriting? There are two methods of handwriting analysis: Graphology is the method of psychological analysis, while forensic document examination or handwriting identification which is the examination of documents and writing samples by a known source, or person. In this paper we have carried out research of the various state of the art technologies available in analyzing an individual's behavior based on their handwriting and the effectiveness of predicting the character and personality of that individual. We also wanted to determine if we can uncover handedness, authorship and gender through analysis. Apart from working on Lewinson-Zubin method of analyzing handwriting, various online tools are also available for handwriting analysis, such as: NEURO SCRIPT, WANDA, CEDAR-FOX, and Gaussian Mixture Model.
Self-compassion, body image, and disordered eating: A review of the literature.

Self-compassion, treating oneself as a loved friend might, demonstrates beneficial associations with body image and eating behaviors. In this systematic review, 28 studies supporting the role of self-compassion as a protective factor against poor body image and eating pathology are reviewed. Findings across various study designs consistently linked self-compassion to lower levels of eating pathology, and self-compassion was implicated as a protective factor against poor body image and eating pathology, with a few exceptions. These findings offer preliminary support that self-compassion may protect against eating pathology by: (a) decreasing eating disorder-related outcomes directly; (b) preventing initial occurrence of a risk factor of a maladaptive outcome; (c) interacting with risk factors to interrupt their deleterious effects; and (d) disrupting the mediational chain through which risk factors operate. We conclude with suggestions for future research that may inform intervention development, including the utilization of research designs that better afford causal inference.
Mars Sample Return campaign status

The proposed Mars Sample Return (MSR) Campaign would be the next big step in the Mars Exploration Program (MEP). Identified by the Planetary Decadal Survey of 2011 as the highest science priority for large missions, the Mars Program has been working in partnership with the European Space Agency (ESA) to define a campaign to return a surface sample of martian surface material to Earth for analysis. As currently envisioned, the MSR campaign concept consists of a series of flight missions to collect and return the sample, and a ground based project to develop a sample receiving facility. The first mission in this proposed campaign would be baselined for launch in 2018, and would consist of a rover capable of scientifically selecting and caching the sample for retrieval by a later mission. Subsequent missions would retrieve the cache, launch it into orbit around Mars, where it would be captured by an orbiter and returned to the Earth. This paper discusses the current status of the MSR Campaign architecture definition and requirements development and near term plans for technical definition and review. Also discussed are selected design drivers and proposed key requirements which would have to be addressed in the definition of the campaign.1, 2
Entity-Relationship Extraction from Wikipedia Unstructured Text

Wikipedia has been the primary source of information for many automatically-generated Semantic Web data sources. However, they suffer from incompleteness since they largely do not cover information contained in the unstructured texts of Wikipedia. Our goal is to extract structured entity-relationships in RDF from such unstructured texts, ultimately using them to enrich existing data sources. Our extraction technique is aimed to be topic-independent, leveraging grammatical dependency of sentences and semantic refinement. Preliminary evaluations of the proposed approach have shown some promising results.
Trial for RDF: adapting graph query languages for RDF data

Querying RDF data is viewed as one of the main applications of graph query languages, and yet the standard model of graph databases -- essentially labeled graphs -- is different from the triples-based model of RDF. While encodings of RDF databases into graph data exist, we show that even the most natural ones are bound to lose some functionality when used in conjunction with graph query languages. The solution is to work directly with triples, but then many properties taken for granted in the graph database context (e.g., reachability) lose their natural meaning.
 Our goal is to introduce languages that work directly over triples and are closed, i.e., they produce sets of triples, rather than graphs. Our basic language is called TriAL, or Triple Algebra: it guarantees closure properties by replacing the product with a family of join operations. We extend TriAL with recursion, and explain why such an extension is more intricate for triples than for graphs. We present a declarative language, namely a fragment of datalog, capturing the recursive algebra. For both languages, the combined complexity of query evaluation is given by low-degree polynomials. We compare our languages with relational languages, such as finite-variable logics, and previously studied graph query languages such as adaptations of XPath, regular path queries, and nested regular expressions; many of these languages are subsumed by the recursive triple algebra. We also provide examples of the usefulness of TriAL in querying graph and RDF data.
Hierarchical Committee of Deep CNNs with Exponentially-Weighted Decision Fusion for Static Facial Expression Recognition

We present a pattern recognition framework to improve committee machines of deep convolutional neural networks (deep CNNs) and its application to static facial expression recognition in the wild (SFEW). In order to generate enough diversity of decisions, we trained multiple deep CNNs by varying network architectures, input normalization, and weight initialization as well as by adopting several learning strategies to use large external databases. Moreover, with these deep models, we formed hierarchical committees using the validation-accuracy-based exponentially-weighted average (VA-Expo-WA) rule. Through extensive experiments, the great strengths of our committee machines were demonstrated in both structural and decisional ways. On the SFEW2.0 dataset released for the 3rd Emotion Recognition in the Wild (EmotiW) sub-challenge, a test accuracy of 57.3% was obtained from the best single deep CNN, while the single-level committees yielded 58.3% and 60.5% with the simple average rule and with the VA-Expo-WA rule, respectively. Our final submission based on the 3-level hierarchy using the VA-Expo-WA achieved 61.6%, significantly higher than the SFEW baseline of 39.1%.
Human-computer interaction for hybrid carving

In this paper we explore human-computer interaction for carving, building upon our previous work with the FreeD digital sculpting device. We contribute a new tool design (FreeD V2), with a novel set of interaction techniques for the fabrication of static models: personalized tool paths, manual overriding, and physical merging of virtual models. We also present techniques for fabricating dynamic models, which may be altered directly or parametrically during fabrication. We demonstrate a semi-autonomous operation and evaluate the performance of the tool. We end by discussing synergistic cooperation between human and machine to ensure accuracy while preserving the expressiveness of manual practice.
A New Iterative Secret Key Cryptosystem Based on Reversible and Irreversible Cellular Automata

Many cryptosystems have been released to secure electronic data on internet. Some data are very critical to be transmitted as plaintext. Thus, to ensure the data confidentiality and integrity, a list of cryptosystems have been elaborated. The most important ones are divided into two categories: symmetric algorithms encrypting and decrypting data in blocks using a single secret key; and asymmetric algorithms using public keys to cipher texts and secret keys to reconstruct plaintexts. The of the present work is the design and implement a new secret key cryptosystem encrypting and decrypting data in blocks according to a number of iterations. Each plaintext block is encrypted using cellular automata and a list of sub keys deduced from a secret key through cellular automata. To demonstrate the feasibility, the proposed scheme is compared with AES algorithm, the well-known symmetric block cipher. We prove that our algorithm resists against statistical attacks and it is faster than AES-256 achieving good confusion and diffusion tests.
Novelty detection: a review - part 1: statistical approaches

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide stateof-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.
Shopping cart injuries, entrapment, and childhood fatality.

Shopping carts may be associated with a variety of injuries, particularly in toddlers and young children. These usually relate to falls from carts or to tip-overs. Injuries that are sustained include hematomas/contusions, abrasions, lacerations, fractures, and fingertip amputations. Fatal episodes are uncommon and are usually due to blunt craniocerebral trauma from falls. A case involving a 19-month-old girl is reported who became entrapped when she inserted her head through the side frame of a cart that had been removed from a supermarket and left at her home address. Death was caused by neck compression. Although rare, the potential for lethal entrapment during unsupervised play means that the presence of stray shopping carts at private residences and in public places, including playgrounds and parks, is of concern. Strategies, such as coin deposits, should be encouraged to assist in the return of such carts to supermarkets.
A deep-learning approach to facial expression recognition with candid images

To recognize facial expression from candid, non-posed images, we propose a deep-learning based approach using convolutional neural networks (CNNs). In order to evaluate the performance in real-time candid facial expression recognition, we have created a candid image facial expression (CIFE) dataset, with seven types of expression in more than 10,000 images gathered from the Web. As baselines, two feature-based approaches (LBP+SVM, SIFT+SVM) are tested on the dataset. The structure of our proposed CNN-based approach is described, and a data augmentation technique is provided in order to generate sufficient number of training samples. The performance using the feature-based approaches is close to the state of the art when tested with standard datasets, but fails to function well when dealing with candid images. Our experiments show that the CNN-based approach is very effective in candid image expression recognition, significantly outperforming the baseline approaches, by a 20% margin.
Reduction of unbalanced axial magnetic force in post-fault operation of a novel six-phase double-stator axial flux PM machine using model predictrive control

This paper investigated the post-fault operation of a novel six-phase double-stator axial flux permanent magnet machine with detached winding configuration, which was found to be superior to existing winding configuration in previous study. However, the unbalanced magnetic force problem still remains unsolved. In this paper, the axial force balancing control principle is proposed and a group of specific current waveforms are deduced. When applying these currents under post-fault condition, magnetic torque, axial magnetic force and rotor losses of the machine are calculated in finite element analysis. The results are compared with normal condition and commonly-used post-fault current waveforms. It is verified that this method reduced the unbalanced axial magnetic force immensely and the torque ripple was also kept at a low level. In order to achieve the proposed current waveform, finite control set model predictive control (FCS-MPC) is adopted. This paper proposed the post-fault model of dual three-phase permanent magnet machines and designed a cost function to track the desired current waveforms. The model of the machine is used to predict the future behavior of the controlled variables and the cost function decides the next step of the inverter by evaluating all the predictions. At last, it is verified by simulation results that the control strategy performs well in both dynamic and steady-state situations.
Application of Pretrained Deep Neural Networks to Large Vocabulary Speech Recognition

The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN/HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN/HMM system trained on two datasets that are much larger than any reported previously with DBN-pretrained ANN/HMM systems 5870 hours of Voice Search and 1400 hours of YouTube data. On the first dataset, the pretrained ANN/HMM system outperforms the best Gaussian Mixture Model Hidden Markov Model (GMM/HMM) baseline, built with a much larger dataset by 3.7% absolute WER, while on the second dataset, it outperforms the GMM/HMM baseline by 4.7% absolute. Maximum Mutual Information (MMI) fine tuning and model combination using Segmental Conditional Random Fields (SCARF) give additional gains of 0.1% and 0.4% on the first dataset and 0.5% and 0.9% absolute on the second dataset.
Analytical Design of Decoupling Internal Model Control ( IMC ) Scheme for Two-Input-Two-Output ( TITO ) Processes with Time Delays

In this paper, a new decoupling control scheme, in terms of an internal model control (IMC) structure, is proposed for two-input -two-output (TITO) processes with time delays. Noteworthy improvement of the decoupling regulation can be achieved for the nominal system output responses, and moreover, either of the system output responses can be quantitatively regulated by virtue of the analytical relationship between the adjustable parameters of the decoupling controller matrix and the nominal system transfer matrix. The ideally optimal controller matrix is analytically derived by proposing the practically desired diagonal system transfer matrix, in terms of the robust H 2 optimal performance objective. Because of the difficulty of physical implementation, its practical form is carefully configured according to whether there exist any complex righthalf-plane (RHP) zeros in the process transfer matrix determinant. At the same time, tuning constraints for the proposed decoupling controller matrix to hold the control system robust stability are analyzed in the presence of the process additive and multiplicative uncertainties, and, accordingly, the on-line tuning rule is provided to cope with the process unmodeled dynamics in practice. Finally, illustrative simulation examples are included to demonstrate the remarkable superiority of the proposed method.
Predicting retweet count using visual cues

Social media platforms allow rapid information diffusion, and serve as a source of information to many of the users. Particularly, in Twitter information provided by tweets diffuses over the users through retweets. Hence, being able to predict the retweet count of a given tweet is important for understanding and controlling information diffusion on Twitter. Since the length of a tweet is limited to 140 characters, extracting relevant features to predict the retweet count is a challenging task. However, visual features of images linked in tweets may provide predictive features. In this study, we focus on predicting the expected retweet count of a tweet by using visual cues of an image linked in that tweet in addition to content and structure-based features.
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation

Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems utilizing complicated multi-step procedures, including refinement networks and complex graphical models. We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair-based connectivity prediction task. Following the intuition that salient objects can be naturally grouped via semantic-aware connectivity between neighboring pixels, we propose a pure Connectivity Net (ConnNet). ConnNet predicts the connectivity probabilities of each pixel with its neighboring pixels by leveraging multi-level cascade contexts embedded in the image and long-range pixel relations. We investigate our approach on two tasks, namely, salient object segmentation and salient instance-level segmentation, and illustrate that consistent improvements can be obtained by modeling these tasks as connectivity instead of binary segmentation tasks for a variety of network architectures. We achieve the state-of-the-art performance, outperforming or being comparable to existing approaches while reducing inference time due to our less complex approach.
Ending Violence against Women and Girls – Protecting Human

Anette Funk is a political scientist specialising in gender and women's rights issues. She worked for several years for a German NGO which provides support to female victims of trafficking and violence. Anette currently works with the project "Strengthening Women's Rights" at GTZ. Her work focuses mainly on issues concerning gender-based violence and women's political participation. James L. Lang is a consultant and trainer working on issues of poverty, gender equality and ending gender-based violence. James has worked with numerous development organisations including UNDP, Oxfam GB, United Nations INSTRAW and GTZ. He is the author of a number of publications on gender-based violence and men as partners in gender equality. He is currently working for UNDP in Southeast Asia. Juliane Osterhaus has a degree in sociology and completed her postgraduate studies at the German Development Institute. Since 1990, she has been working in various positions at headquarters and in Africa for GTZ focusing on gender and human rights issues, participation and socioculture. At present she is directing the two supra-regional projects " Strengthening Women's Rights " and " Realizing Human Rights in German Development Cooperation " .
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.