nils-herrmann/clean_scirepeval huggingface dataset:
X-shaped inferior extensor retinaculum and its doubtful use in the Bröstrom–Gould procedure

The inferior extensor retinaculum (IER) is an aponeurotic structure located in the anterior aspect of the ankle. According to the literature, it can be used to reinforce a repair of the anterior talofibular ligament in ankle instability. Despite its usual description as an Y-shaped structure, it is still unclear which part of the retinaculum is used for this purpose, or if it is instead the crural fascia that is being used. The purpose of this study is to define the anatomical characteristics of the IER to better understand its role in the Broström–Gould procedure. Twenty-one ankles were dissected. The morphology of the IER and its relationship with neighbouring structures were recorded. Seventeen (81%) of the IER in this study had an X-shaped morphology, with the presence of an additional oblique superolateral band. This band, by far the thinnest of the retinaculum, is supposed to be used to reinforce the repair of the anterior talofibular ligament. The intermediate dorsal cutaneous nerve (lateral branch of the superficial peroneal nerve) was found to cross the retinaculum in all cases. The IER is most commonly seen as an X-shaped structure, but the fact that the oblique superolateral band is a thin band of tissue probably indicates that it may not add significant strength to ankle stability. Furthermore, the close relationship of the retinaculum with the superficial peroneal nerve is another factor to consider before deciding to perform a Broström–Gould procedure. These anatomical findings advise against the use of the Gould augmentation.
Multilingual speech-to-speech translation system for mobile consumer devices

Along with the advancement of speech recognition technology and machine translation technology in addition to the fast distribution of mobile devices, speech-to-speech translation technology no longer remains as a subject of research as it has become popularized throughout many users. In order to develop a speech-to-speech translation system that can be widely used by many users, however, the system needs to reflect various characteristics of utterances by the users who are actually to use the speech-to-speech translation system other than improving the basic functions under the experimental environment. This study has established a massive language and speech database closest to the environment where speech-to- speech translation device actually is being used after mobilizing plenty of people based on the survey on users' demands. Through this study, it was made possible to secure excellent basic performance under the environment similar to speech-to-speech translation environment, rather than just under the experimental environment. Moreover, with the speech-to-speech translation UI, a user-friendly UI has been designed; and at the same time, errors were reduced during the process of translation as many measures to enhance user satisfaction were employed. After implementing the actual services, the massive database collected through the service was additionally applied to the system following a filtering process in order to procure the best-possible robustness toward both the details and the environment of the users' utterances. By applying these measures, this study is to unveil the procedures where multi-language speech-to-speech translation system has been successfully developed for mobile devices.
Towards personalized smart city guide services in future internet environments

The FI-CONTENT project aims at establishing the foundation of a European infrastructure for developing and testing novel smart city services. The Smart City Services Platform will develop enabling technology for SMEs and developer to create services offering residents and visitors to cities smart services that enhance their city visit or daily life. We have made use of generic, specific and common enablers to develop a reference implementation, the Smart City Guide web app. The basic information is provided by the Open City Database, an open source specific enabler that can be used for any city in Europe. Recommendation as a Service is an enabler that can be applied to lots use cases, here we describe how we integrated it into the Smart City Guide. The uses cases will be iteratively improved and upgraded during regular iterative cycles based on feedback gained in lab and field trials at the experimentation sites. As the app is transferable to any city, it will be tested at a number of experimentation sites.
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

We present an unsupervised method for learning a hierarchy of sparse feature detectors that are invariant to small shifts and distortions. The resulting feature extractor consists of multiple convolution filters, followed by a feature-pooling layer that computes the max of each filter output within adjacent windows, and a point-wise sigmoid non-linearity. A second level of larger and more invariant features is obtained by training the same algorithm on patches of features from the first level. Training a supervised classifier on these features yields 0.64% error on MNIST, and 54% average recognition rate on Caltech 101 with 30 training samples per category. While the resulting architecture is similar to convolutional networks, the layer-wise unsupervised training procedure alleviates the over-parameterization problems that plague purely supervised learning procedures, and yields good performance with very few labeled training samples.
Sentiment analysis and affective computing for depression monitoring

Depression is one of the most common and disabling mental disorders that has a relevant impact on society. Semiautomatic and/or automatic health monitoring systems could be crucial and important to improve depression detection and follow-up. Sentiment Analysis refers to the use of natural language processing and text mining methodologies aiming to identify opinion or sentiment. Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Sentiment Analysis and Affective Computing methodologies could provide effective tools and systems for an objective assessment and monitoring of psychological disorders and, in particular, of depression. In this paper, the application of sentiment analysis and affective computing methodologies to depression detection and monitoring are presented and discussed. Moreover, a preliminary design of an integrated multimodal system for depression monitoring, that includes sentiment analysis and affective computing techniques, is proposed. Specifically, the paper outlines the main issues and challenges relative to the design of such a system.
Active vision in robotic systems: A survey of recent developments

In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.
CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets

Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an e cient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single pre x path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is e cient and scalable over large databases, and is faster than the previously proposed methods.
Multi-document Summarization via Budgeted Maximization of Submodular Functions

We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can efficiently solve the budgeted submodular maximization problem near-optimally, and we derive new approximation bounds in doing so. Experiments on DUC’04 task show that our approach is superior to the bestperforming method from the DUC’04 evaluation on ROUGE-1 scores.
Regenerative braking strategy for electric vehicles

Regenerative braking is an effective approach for electric vehicles to extend their driving range. The control strategy of regenerative braking plays an important role in maintaining the vehicle's stability and recovering energy. In this paper, the main properties that have influence on brake energy regeneration are analyzed. Mathematical model of brake energy regenerating electric vehicles is established. By analyzing the charge and discharge characteristics of the battery and motor, a simple regenerative braking strategy is proposed. The strategy takes the braking torque required, the motor available braking torque, and the braking torque limit into account, and it can make the best use of the motor braking torque. Simulation results show higher energy regeneration compared to a parallel strategy when the proposed strategy is adopted.
Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods

Precise financial series predicting has long been a difficult problem because of unstableness and many noises within the series. Although Traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine Learning, as an emerging research field in recent years, has brought about many incredible improvements in tasks such as regressing and classifying, and it’s also promising to exploit the methodology in financial time series predicting. In this paper, the predicting precision of financial time series between traditional time series models and mainstream machine learning models including some state-of-the-art ones of deep learning are compared through experiment using real stock index data from history. The result shows that machine learning as a modern method far surpasses traditional models in precision.
Intelligent Heart Disease Prediction System Using CANFIS and Genetic Algorithm

Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease. CANFIS, Genetic Algorithms (GA), Heart disease, Membership Function (MF).
Experiments With Some Programs That Search Game Trees

Many problems in artificial intelligence involve the searching of large trees of alternative possibilities—for example, game-playing and theorem-proving. The problem of efficiently searching large trees is discussed. A new method called “dynamic ordering” is described, and the older minimax and Alpha-Beta procedures are described for comparison purposes. Performance figures are given for six variations of the game of kalah. A quantity called “depth ratio” is derived which is a measure of the efficiency of a search procedure. A theoretical limit of efficiency is calculated and it is shown experimentally that the dynamic ordering procedure approaches that limit.
Dynamic path planning in sensor-based terrain acquisition

Ahtmct-The problem of terrain acquisition presents a special case of robot motion planning. In it, a robot that operates in an unfamiliar scene populated with a finite number of objects (obstacles) of unknown shapes and dimensions is asked to cover the scene and/or build its complete map using some sort of sensory feedback (e.g., vision) and generating as short a path during the operation as possible. Algorithms considered thus far in literature make strong assumptions about the obstaclesfor example, that they are polygonaland measure the algorithm performance in terms of the number of constraints describing obstacles, such as the number of obstacle vertices. In this paper, the terrain acquisition problem is formulated as that of continuous motion planning, and no constraints are imposed on obstacle geometry. Two algorithms are described for acquiring planar terrains with obstacles of arbitrary shape. Estimates of the algorithm performance are derived as upper bounds on the lengths of generated paths.
Bias and ignorance in demographic perception

When it comes to knowledge of demographic facts, misinformation appears to be the norm. Americans massively overestimate the proportions of their fellow citizens who are immigrants, Muslim, LGBTQ, and Latino, but underestimate those who are White or Christian. Previous explanations of these estimation errors have invoked topic-specific mechanisms such as xenophobia or media bias. We reconsidered this pattern of errors in the light of more than 30 years of research on the psychological processes involved in proportion estimation and decision-making under uncertainty. In two publicly available datasets featuring demographic estimates from 14 countries, we found that proportion estimates of national demographics correspond closely to what is found in laboratory studies of quantitative estimates more generally. Biases in demographic estimation, therefore, are part of a very general pattern of human psychology—independent of the particular topic or demographic under consideration—that explains most of the error in estimates of the size of politically salient populations. By situating demographic estimates within a broader understanding of general quantity estimation, these results demand reevaluation of both topic-specific misinformation about demographic facts and topic-specific explanations of demographic ignorance, such as media bias and xenophobia.
Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism

Dopamine (DA) depletion in the basal ganglia (BG) of Parkinson's patients gives rise to both frontal-like and implicit learning impairments. Dopaminergic medication alleviates some cognitive deficits but impairs those that depend on intact areas of the BG, apparently due to DA overdose. These findings are difficult to accommodate with verbal theories of BG/DA function, owing to complexity of system dynamics: DA dynamically modulates function in the BG, which is itself a modulatory system. This article presents a neural network model that instantiates key biological properties and provides insight into the underlying role of DA in the BG during learning and execution of cognitive tasks. Specifically, the BG modulates the execution of actions (e.g., motor responses and working memory updating) being considered in different parts of the frontal cortex. Phasic changes in DA, which occur during error feedback, dynamically modulate the BG threshold for facilitating/suppressing a cortical command in response to particular stimuli. Reduced dynamic range of DA explains Parkinson and DA overdose deficits with a single underlying dysfunction, despite overall differences in raw DA levels. Simulated Parkinsonism and medication effects provide a theoretical basis for behavioral data in probabilistic classification and reversal tasks. The model also provides novel testable predictions for neuropsychological and pharmacological studies, and motivates further investigation of BG/DA interactions with the prefrontal cortex in working memory.
Ocular symptom detection using smartphones

It is often said that "the eyes are the windows to the soul". Although this usually metaphorically refers to a person's emotional state, there is literal truth in this statement with reference to a person's biological state. The eyes are dynamic and sensitive organs that are connected to the rest of the body via the circulatory and nervous systems. This means that diseases originating from an organ within the body can indirectly affect the eyes. Although the eyes may not be the origin for many such diseases, they are visible to external observers, making them an accessible point of diagnosis. For instance, broken blood vessels in the sclera could indicate a blood clotting disorder or a vitamin K deficiency. Instead of "windows to the soul", the eyes are "sensors to the body" that can be monitored to screen for certain diseases.
An Efficient Approach to Model-Based Hierarchical Reinforcement Learning

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.
Fusion of Keypoint Tracking and Facial Landmark Detection for Real-Time Head Pose Estimation

In this paper, we address the problem of extreme head pose estimation from intensity images, in a monocular setup. We introduce a novel fusion pipeline to integrate into a dedicated Kalman Filter the pose estimated from a tracking scheme in the prediction stage and the pose estimated from a detection scheme in the correction stage. To that end, the measurement covariance of the Kalman Filter is updated in every frame. The tracking scheme is performed using a set of keypoints extracted in the area of the head along with a simple 3D geometric model. The detection scheme, on the other hand, relies on the alignment of facial landmarks in each frame combined with 3D features extracted on a head mesh. The head pose in each scheme is estimated by minimizing the reprojection error from the 3D-2D correspondences. By combining both frameworks, we extend the applicability of head pose estimation from facial landmarks to cases where these features are no longer visible. We compared the proposed method to other related approaches, showing that it can achieve state-of-the-art performance. We also demonstrate that our approach is suitable for cases with extreme head rotations and (self-) occlusions, besides being suitable for real time applications.
DIFusion: Fast Skip-Scan with Zero Space Overhead

Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.
Microrobot Design Using Fiber Reinforced Composites

Mobile microrobots with characteristic dimensions on the order of 1cm are difficult to design using either MEMS (microelectromechanical systems) technology or precision machining. This is due to the challenges associated with constructing the high strength links and high-speed, low-loss joints with micron scale features required for such systems. Here we present an entirely new framework for creating microrobots which makes novel use of composite materials. This framework includes a new fabrication process termed Smart Composite Microstructures (SCM) for integrating rigid links and large angle flexure joints through a laser micromachining and lamination process. We also present solutions to actuation and integrated wiring issues at this scale using SCM. Along with simple design rules that are customized for this process, our new complete microrobotic framework is a cheaper, quicker, and altogether superior method for creating microrobots that we hope will become the paradigm for robots at this scale.
Observation and analysis of BGP behavior under stress

Despite BGP's critical importance as the de-facto Internet inter-domain routing protocol, there is little understanding of how BGP actually performs under stressful conditions when dependable routing is most needed. In this paper, we examine BGP's behavior during one stressful period, the Code Red/Nimda attack on September 18, 2001. The attack was correlated with a 30-fold increase in the BGP update messages at a monitoring point which peers with a number of Internet service providers. Our examination of BGP's behavior during the event concludes that BGP exhibited no significant abnormality, and that over 40% of the observed updates can be attributed to the monitoring artifact in current BGP measurement settings. Our analysis, however, does reveal several weak points in both the protocol and its implementation, such as BGP's sensitivity to the transport session reliability, its inability to avoid the global propagation of small local changes, and its certain implementation features whose otherwise benign effects only get amplified under stressful conditions. We also identify areas for improvement in the current network measurement and monitoring effort.