nils-herrmann/clean_scirepeval huggingface dataset:
Word Salad: Relating Food Prices and Descriptions

We investigate the use of language in food writing, specifically on restaurant menus and in customer reviews. Our approach is to build predictive models of concrete external variables, such as restaurant menu prices. We make use of a dataset of menus and customer reviews for thousands of restaurants in several U.S. cities. By focusing on prediction tasks and doing our analysis at scale, our methodology allows quantitative, objective measurements of the words and phrases used to describe food in restaurants. We also explore interactions in language use between menu prices and sentiment as expressed in user reviews.
Mining Version Histories for Detecting Code Smells

Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase changeand fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change overtime. In this paper, we propose Historical Information for Smell deTection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. The first, conducted on 20 open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72 and 86 percent, and its recall ranges between 58 and 100 percent. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system's snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers' perception of poor design and implementation choices. We involved 12 developers of four open source projects that recognized more than 75 percent of the code smell instances identified by HIST as actual design/implementation problems.
XLynx - An FPGA-based XML filter for hybrid XQuery processing

While offering unique performance and energy-saving advantages, the use of Field-Programmable Gate Arrays (FPGAs) for database acceleration has demanded major concessions from system designers. Either the programmable chips have been used for very basic application tasks (such as implementing a rigid class of selection predicates) or their circuit definition had to be completely recompiled at runtime—a very CPU-intensive and time-consuming effort.
 This work eliminates the need for such concessions. As part of our XLynx implementation—an FPGA-based XML filter—we present skeleton automata, which is a design principle for data-intensive hardware circuits that offers high expressiveness and quick reconfiguration at the same time. Skeleton automata provide a generic implementation for a class of finite-state automata. They can be parameterized to any particular automaton instance in a matter of microseconds or less (as opposed to minutes or hours for complete recompilation).
 We showcase skeleton automata based on XML projection [Marian and Siméon 2003], a filtering technique that illustrates the feasibility of our strategy for a real-world and challenging task. By performing XML projection in hardware and filtering data in the network, we report on performance improvements of several factors while remaining nonintrusive to the back-end XML processor (we evaluate XLynx using the Saxon engine).
Big brother for bees (3B) — Energy neutral platform for remote monitoring of beehive imagery and sound

Honey bees have played a major role in the history and development of humankind, in particular for nutrition and agriculture. The most important role of the western honey bee (Apis mellifera) is that of pollination. A large amount of crops consumed throughout the world today are pollinated by the activity of the honey bee. It is estimated that the total value of these crops stands at 155 billion euro annually. The goal of the work outlined in this paper was to use wireless sensor network technology to monitor a colony within the beehive with the aim of collecting image and audio data. These data allows the beekeeper to obtain a much more comprehensive view of the in-hive conditions, an indication of flight direction, as well as monitoring the hive outside of the traditional beekeeping times, i.e. during the night, poor weather, and winter months. This paper outlines the design of a fully autonomous beehive monitoring system which provided image and sound monitoring of the internal chambers of the hive, as well as a warning system for emergency events such as possible piping, dramatically increased hive activity, or physical damage to the hive. The final design included three wireless nodes: a digital infrared camera with processing capabilities for collecting imagery of the hive interior; an external thermal imaging camera node for monitoring the colony status and activity, and an accelerometer and a microphone connected to an off the shelf microcontroller node for processing. The system allows complex analysis and sensor fusion. Some scenarios based on sound processing, image collection, and accelerometers are presented. Power management was implemented which allowed the system to achieve energy neutrality in an outdoor deployment with a 525 × 345 mm solar panel.
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding

Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, post-processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG compressed images. In this paper, we present a CNN-based post-processing algorithm for High Efficiency Video Coding (HEVC), the state-of-theart video coding standard. We redesign a Variable-filter-size Residuelearning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit-rate reduction, lower memory cost, and multiplied computational speedup.
Light-weight localization for vehicles using road markings

Traditional vision-based localization methods such as visual SLAM suffer from practical problems in outdoor environments such as unstable feature detection and inability to perform location recognition under lighting, perspective, weather and appearance change. Additionally map construction on a large scale in these systems presents its own challenges. In this work, we present a novel method for precisely localizing vehicles on the road using signs marked on the road (road markings), which have the advantage of being distinct and easy to detect, their detection being robust under changes in lighting and weather. Our method uses corners detected on road markings to perform localization in global coordinates. The method consists of two phases - a mapping phase when a high-quality GPS device is used to automatically survey road marks and add them to a light-weight “map” or database, and a localization phase where road mark detection and look-up in the map, combined with visual odometry, produces precise localization. We present experiments using a real-time implementation operating in a car that demonstrates the improved localization robustness and accuracy of our system even when using road marks alone. However, in this case the trajectory between road marks has to be filled-in by visual odometry, which contributes drift. Hence, we also present a mechanism for combining road-mark-based maps with sparse feature-based maps that results in greater accuracy still. We see our use of road marks as a significant step in the general trend of using higher-level features for improved localization performance irrespective of environment conditions.
A New Class of Antenna Array With a Reconfigurable Element Factor

A parasitic layer-based multifunctional reconfigurable antenna array (MRAA) formed by the linear combination of four (4 <formula formulatype="inline"> <tex Notation="TeX">$\times$</tex></formula> 1) identical multifunctional reconfigurable antenna (MRA) elements is presented. Each MRA produces eight modes of operation corresponding to three steerable beam directions (<formula formulatype="inline"><tex Notation="TeX">$\theta_{xz}=-30^{\circ}$</tex></formula>, 0<formula formulatype="inline"><tex Notation="TeX">$^{\circ}$</tex></formula>, 30<formula formulatype="inline"><tex Notation="TeX">$^{\circ}$</tex></formula>) with linear and circular polarizations in <formula formulatype="inline"><tex Notation="TeX">$x-z$</tex></formula> plane and another two steerable beam directions (<formula formulatype="inline"><tex Notation="TeX">$\theta_{yz}=-30^{\circ}$</tex> </formula>, 30<formula formulatype="inline"><tex Notation="TeX">$^{\circ}$</tex> </formula>) in <formula formulatype="inline"><tex Notation="TeX">$y-z$</tex> </formula> plane with linear polarization. An individual MRA consists of an aperture-coupled driven patch antenna with a parasitic layer placed above it. The surface of the parasitic layer has a grid of 4 <formula formulatype="inline"> <tex Notation="TeX">$\times$</tex></formula> 4 electrically-small rectangular-shaped metallic pixels. The adjacent pixels can be connected/disconnected by means of switching resulting in reconfigurability in beam-direction and polarization. A 4 <formula formulatype="inline"><tex Notation="TeX">$\times$</tex></formula> 1 linear MRAA operating in the <formula formulatype="inline"><tex Notation="TeX">$\sim$</tex> </formula>5.4–5.6 GHz is formed by the optimized MRA elements. MRA and MRAA prototypes have been fabricated and measured. The measured and simulated results agree well indicating <formula formulatype="inline"><tex Notation="TeX">$\sim$</tex> </formula>13.5 dB realized array gain and <formula formulatype="inline"><tex Notation="TeX">$\sim$</tex></formula>3% common bandwidth. The MRAA presents some advantages as compared to a standard antenna array: MRAA alleviates the scan loss inherit to standard antenna arrays, provides higher gain, does not need phase shifters for beam steering in certain plane, and is capable of polarization reconfigurability.
Fast Image Super-Resolution Based on In-Place Example Regression

We propose a fast regression model for practical single image super-resolution based on in-place examples, by leveraging two fundamental super-resolution approaches- learning from an external database and learning from self-examples. Our in-place self-similarity refines the recently proposed local self-similarity by proving that a patch in the upper scale image have good matches around its origin location in the lower scale image. Based on the in-place examples, a first-order approximation of the nonlinear mapping function from low-to high-resolution image patches is learned. Extensive experiments on benchmark and real-world images demonstrate that our algorithm can produce natural-looking results with sharp edges and preserved fine details, while the current state-of-the-art algorithms are prone to visual artifacts. Furthermore, our model can easily extend to deal with noise by combining the regression results on multiple in-place examples for robust estimation. The algorithm runs fast and is particularly useful for practical applications, where the input images typically contain diverse textures and they are potentially contaminated by noise or compression artifacts.
Factors Affecting Malaysian Mobile Banking Adoption : An Empirical Analysis

This empirical study aims to investigate the factors that influence Malaysians’ intention to adopt mobile banking by extending the renowned framework of Technology Acceptance Model (TAM). A self-administrated questionnaire had been developed and distributed in Malaysia. Out of the 400 questionnaires, only 175 useable questionnaires were returned, yielding a response rate of 43.75 percent. Results were subsequently analyzed by using multiple regression and factor analysis. Factors such as perceived usefulness (PU), perceived ease of use (PEOU), relative advantages (RA) and personal innovativeness (PI) were found positively related with the intention to adopt mobile banking services. However, social norms (SN) were the only factor found insignificant. As expected, perceived risks (PR) was negatively associated with the mobile banking adoption. The research findings provide several important implications for banks, service developers, and software engineer with better strategic insights to design and implement mobile banking services to yield higher consumer acceptance towards mobile banking in Malaysia.
Direct torque control of PWM inverter-fed AC motors - a survey

This paper presents a review of recently used direct torque and flux control (DTC) techniques for voltage inverter-fed induction and permanent-magnet synchronous motors. A variety of techniques, different in concept, are described as follows: switching-table-based hysteresis DTC, direct self control, constant-switching-frequency DTC with space-vector modulation (DTC-SVM). Also, trends in the DTC-SVM techniques based on neuro-fuzzy logic controllers are presented. Some oscillograms that illustrate properties of the presented techniques are shown.
Automatic detection of anger in telephone speech with robust autoregressive modulation filtering

A new system for automatic detection of angry speech is proposed. Using simulation of far-end-noise-corrupted telephone speech and the widely used Berlin database of emotional speech, autoregressive prediction of features across speech frames is shown to contribute significantly to both the clean speech performance and the robustness of the system. The autoregressive models are learned from the training data in order to capture long-term temporal dynamics of the features. Additionally, linear predictive spectrum analysis outperforms conventional Fourier spectrum analysis in terms of robustness in the computation of mel-frequency cepstral coefficients in the feature extraction stage.
A dual band (C/Ku) feed system with transmit and receive capability

In satellite earth station antenna systems there is an increasing demand for complex single aperture, multi-function and multi-frequency band capable feed systems. In this work, a multi band feed system (6/12 GHz) is described which employs quadrature junctions (QJ) and supports transmit and receive functionality in the C and Ku bands respectively. This feed system is designed for a 16.4 m diameter shaped cassegrain antenna. It is a single aperture, 4 port system with transmit capability in circular polarization (CP) mode over the 6.625-6.69 GHz band and receive in the linear polarization (LP) mode over the 12.1-12.3 GHz band
A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots

To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair.



In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1h to 2months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before.



In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials.



The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks.



These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.
Directional binary code with application to PolyU near-infrared face database

This paper introduces the establishment of PolyU near-infrared face database (PolyU-NIRFD) and presents a new coding scheme for face recognition. The PolyU-NIRFD contains images from 350 subjects, each contributing about 100 samples with variations of pose, expression, focus, scale, time, etc. In total, 35,000 samples were collected in the database. The PolyU-NIRFD provides a platform for researchers to develop and evaluate various near-infrared face recognition techniques under large scale, controlled and uncontrolled conditions. A new coding scheme, namely directional binary code (DBC), is then proposed for near-infrared face recognition. Finally, we provide three protocols to evaluate and compare the proposed DBC method with baseline face recognition methods, including Gabor based Eigenface, Fisherface and LBP (local binary pattern) on the PolyU-NIRFD database. In addition, we also conduct experiments on the visible light band FERET database to further validate the proposed DBC scheme.
Scalable SPARQL Querying of Large RDF Graphs

The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inefficient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more efficient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster.
Towards Ontology-Based Program Analysis

Program analysis is fundamental for program optimizations, debugging, and many other tasks. But developing program analyses has been a challenging and error-prone process for general users. Declarative program analysis has shown the promise to dramatically improve the productivity in the development of program analyses. Current declarative program analysis is however subject to some major limitations in supporting cooperations among analysis tools, guiding program optimizations, and often requires much effort for repeated program preprocessing. In this work, we advocate the integration of ontology into declarative program analysis. As a way to standardize the definitions of concepts in a domain and the representation of the knowledge in the domain, ontology offers a promising way to address the limitations of current declarative program analysis. We develop a prototype framework named PATO for conducting program analysis upon ontology-based program representation. Experiments on six program analyses confirm the potential of ontology for complementing existing declarative program analysis. It supports multiple analyses without separate program preprocessing, promotes cooperative Liveness analysis between two compilers, and effectively guides some program optimizations.
Generalized Belief Propagation

Yair Weiss Computer Science Division UC Berkeley, 485 Soda Hall Berkeley, CA 94720-1776 Phone: 510-642-5029 yweiss@cs.berkeley.edu Belief propagation (BP) was only supposed to work for tree-like networks but works surprisingly well in many applications involving networks with loops, including turbo codes. However, there has been little understanding of the algorithm or the nature of the solutions it finds for general graphs. We show that BP can only converge to a stationary point of an approximate free energy, known as the Bethe free energy in statistical physics. This result characterizes BP fixed-points and makes connections with variational approaches to approximate inference. More importantly, our analysis lets us build on the progress made in statistical physics since Bethe's approximation was introduced in 1935. Kikuchi and others have shown how to construct more accurate free energy approximations, of which Bethe's approximation is the simplest. Exploiting the insights from our analysis, we derive generalized belief propagation (GBP) versions ofthese Kikuchi approximations. These new message passing algorithms can be significantly more accurate than ordinary BP, at an adjustable increase in complexity. We illustrate such a new GBP algorithm on a grid Markov network and show that it gives much more accurate marginal probabilities than those found using ordinary BP.
Blockchain-based TLS Notary Service

The Transport Layer Security (TLS) protocol is a de facto standard of secure client-server communication on the Internet. Its security can be diminished by a variety of attacks that leverage on weaknesses in its design and implementations. An example of a major weakness is the public-key infrastructure (PKI) that TLS deploys, which is a weakest-link system and introduces hundreds of links (i.e., trusted entities). Consequently, an adversary compromising a single trusted entity can impersonate any website. Notary systems, based on multi-path probing, were early and promising proposals to detect and prevent such attacks. Unfortunately, despite their benefits, they are not widely deployed, mainly due to their long-standing unresolved problems. In this paper, we present Persistent and Accountable Domain Validation (PADVA), which is a next-generation TLS notary service. PADVA combines the advantages of previous proposals, enhancing them, introducing novel mechanisms, and leveraging a blockchain platform which provides new features. PADVA keeps notaries auditable and accountable, introduces service-level agreements and mechanisms to enforce them, relaxes availability requirements for notaries, and works with the legacy TLS ecosystem. We implemented and evaluated PADVA, and our experiments indicate its efficiency and deployability.