nils-herrmann/clean_scirepeval huggingface dataset:
RaceTrack: efficient detection of data race conditions via adaptive tracking

Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has been observed. RaceTrack uses a novel hybrid detection algorithm and employs an adaptive approach that automatically directs more effort to areas that are more suspicious, thus providing more accurate warnings for much less over-head. A post-processing step correlates warnings and ranks code segments based on how strongly they are implicated in potential data races. We implemented RaceTrack inside the virtual machine of Microsoft's Common Language Runtime (product version v1.1.4322) and monitored several major, real-world applications directly out-of-the-box,without any modification. Adaptive tracking resulted in a slowdown ratio of about 3x on memory-intensive programs and typically much less than 2x on other programs,and a memory ratio of typically less than 1.2x. Several serious data race bugs were revealed, some previously unknown.
Miniaturized Dipole antenna using Koch fractal technique for wearable application

In this paper, Koch iteration Technique is applied to a Dipole antenna to get second iteration version of Koch fractal antenna. The performance properties of Koch fractal Dipole for 450MHZ have been primarily indicated to its fractal geometry. The Koch geometry is used to reduce the overall size of the antenna. The size of designed Antenna is reduced around 32% compared to conventional dipole at designed frequency. The Koch fractal antenna is designed, fabricated and characterized on a wearable (jeans) substrate. Simulations are done in CST MICROWAVE STUDIO.
A modified Lyapunov-function based control strategy for three-phase grid-connected VSI with LCL filter

In this paper, a modified Lyapunov-function based control strategy for three-phase grid-connected LCL-filtered voltage source inverter is presented. The grid-connected system is modeled in the synchronously rotating frame. Lyapunov-function based control is derived from the Lyapunov's direct method which guarantees the global stability of the closed-loop system. Although the conventional Lyapunov-function based control strategy offers global stability, it is not able to damp the inherent oscillations that occur on the grid current. Therefore, a modified Lyapunov-function based control strategy is proposed by adding the capacitor voltage feedback into the control loop. In the proposed control strategy, the measurement of inverter currents, capacitor voltages and grid currents are essential. The generation of reference functions in the d- and q-components can be achieved by using the reference d-component grid current. Computer simulation results are presented and reported to validate the proposed control approach.
Keeping track of time: evidence for episodic-like memory in great apes

Episodic memory, as defined by Tulving, can be described in terms of behavioural elements (what, where and when information) but it is also accompained by an awareness of one’s past (chronesthesia) and a subjective conscious experience (autonoetic awareness). Recent experiments have shown that corvids and rodents recall the where, what and when of an event. This capability has been called episodic-like memory because it only fulfils the behavioural criteria for episodic memory. We tested seven chimpanzees, three orangutans and two bonobos of various ages by adapting two paradigms, originally developed by Clayton and colleagues to test scrub jays. In Experiment 1, subjects were fed preferred but perishable food (frozen juice) and less preferred but non-perishable food (grape). After the food items were hidden, subjects could choose one of them either after 5 min or 1 h. The frozen juice was still available after 5 min but melted after 1 h and became unobtainable. Apes chose the frozen juice significantly more after 5 min and the grape after 1 h. In Experiment 2, subjects faced two baiting events happening at different times, yet they formed an integrated memory for the location and time of the baiting event for particular food items. We also included a memory task that required no temporal encoding. Our results showed that apes remember in an integrated fashion what, where and when (i.e., how long ago) an event happened; that is, apes distinguished between different events in which the same food items were hidden in different places at different times. The temporal control of their choices was not dependent on the familiarity of the platforms where the food was hidden. Chimpanzees’ and bonobos’ performance in the temporal encoding task was age-dependent, following an inverted U-shaped distribution. The age had no effect on the performance of the subjects in the task that required no temporal encoding.
Generating code for holistic query evaluation

We present the application of customized code generation to database query evaluation. The idea is to use a collection of highly efficient code templates and dynamically instantiate them to create query- and hardware-specific source code. The source code is compiled and dynamically linked to the database server for processing. Code generation diminishes the bloat of higher-level programming abstractions necessary for implementing generic, interpreted, SQL query engines. At the same time, the generated code is customized for the hardware it will run on. We term this approach holistic query evaluation. We present the design and development of a prototype system called HIQUE, the Holistic Integrated Query Engine, which incorporates our proposals. We undertake a detailed experimental study of the system's performance. The results show that HIQUE satisfies its design objectives, while its efficiency surpasses that of both well-established and currently-emerging query processing techniques.
The Weil Proof and the Geometry of the Adèles Class Space

This paper explores analogies between the Weil proof of the Riemann hypothesis for function fields and the geometry of the adèles class space, which is the noncommutative space underlying Connes’ spectral realization of the zeros of the Riemann zeta function. We consider the cyclic homology of the cokernel (in the abelian category of cyclic modules) of the “restriction map” defined by the inclusion of the idèles class group of a global field in the noncommutative adèles class space. Weil’s explicit formula can then be formulated as a Lefschetz trace formula for the induced action of the idèles class group on this cohomology. In this formulation the Riemann hypothesis becomes equivalent to the positivity of the relevant trace pairing. This result suggests a possible dictionary between the steps in the Weil proof and corresponding notions involving the noncommutative geometry of the adèles class space, with good working notions of correspondences, degree, and codegree etc. In particular, we construct an analog for number fields of the algebraic points of the curve for function fields, realized here as classical points (low temperature KMS states) of quantum statistical mechanical systems naturally associated to the periodic orbits of the action of the idèles class group, that is, to the noncommutative spaces on which the geometric side of the trace formula is supported.
Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP) - Survey and challenges

Wireless sensor networks (WSNs) research has pre-dominantly assumed the use of a portable and limited energy source, viz. batteries, to power sensors. Without energy, a sensor is essentially useless and cannot contribute to the utility of the network as a whole. Consequently, substantial research efforts have been spent on designing energy-efficient networking protocols to maximize the lifetime of WSNs. However, there are emerging WSN applications where sensors are required to operate for much longer durations (like years or even decades) after they are deployed. Examples include in-situ environmental/habitat monitoring and structural health monitoring of critical infrastructures and buildings, where batteries are hard (or impossible) to replace/recharge. Lately, an alternative to powering WSNs is being actively studied, which is to convert the ambient energy from the environment into electricity to power the sensor nodes. While renewable energy technology is not new (e.g., solar and wind) the systems in use are far too large for WSNs. Those small enough for use in wireless sensors are most likely able to provide only enough energy to power sensors sporadically and not continuously. Sensor nodes need to exploit the sporadic availability of energy to quickly sense and transmit the data. This paper surveys related research and discusses the challenges of designing networking protocols for such WSNs powered by ambient energy harvesting.
DPICO: a high speed deep packet inspection engine using compact finite automata

Deep Packet Inspection (DPI)has been widely adopted in detecting network threats such as intrusion, viruses and spam. It is challenging, however, to achieve high speed DPI due to the expanding rule sets and ever increasing line rates. A key issue is that the size of the finite automata falls beyond the capacity of on-chip memory thus incurring expensive off-chip accesses. In this paper we present DPICO a hardware based DPI engine that utilizes novel techniques to minimize the storage requirements for finite automata. The techniques proposed are modified content addressable memory (mCAM), interleaved memory banks, and data packing. The experiment results show the scalable performance of DPICO can achieve up to 17.7 Gbps throughput using a contemporary FPGA chip. Experiment data also show that a DPICO based accelerator can improve the pattern matching performance of a DPI server by up to 10 times.
Teaching Introductory Artificial Intelligence with Pac-Man

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the PacMan environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.
Building Lexical Vector Representations from Concept Definitions

The use of distributional language representations have opened new paths in solving a variety of NLP problems. However, alternative approaches can take advantage of information unavailable through pure statistical means. This paper presents a method for building vector representations from meaning unit blocks called concept definitions, which are obtained by extracting information from a curated linguistic resource (Wiktionary). The representations obtained in this way can be compared through conventional cosine similarity and are also interpretable by humans. Evaluation was conducted in semantic similarity and relatedness test sets, with results indicating a performance comparable to other methods based on single linguistic resource extraction. The results also indicate noticeable performance gains when combining distributional similarity scores with the ones obtained using this approach. Additionally, a discussion on the proposed method’s shortcomings is provided in the analysis of error cases.
Multimodal KB Harvesting for Emerging Spatial Entities

New entities are being created daily. Though the novelty of these entities naturally attracts mentions, due to lack of prior knowledge, it is more challenging to collect knowledge about such entities than pre-existing entities, whose KBs are comprehensively annotated through LBSNs and EBSNs. In this paper, we focus on knowledge harvesting for emerging spatial entities (ESEs), such as new businesses and venues, assuming we have only a list of ESE names. Existing techniques for knowledge base (KB) harvesting are primarily associated with information extraction from textual corpora. In contrast, we propose a multimodal method for event detection based on the complementary interaction of image, text, and user information between multi-source platforms, namely Flickr and Twitter. We empirically validate our harvesting approaches improve the quality of KB with enriched place and event knowledge.
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.
Gain Enhancement of a Broadband Symmetrical Dual-Loop Antenna Using Shorting Pins

A broadband symmetrical dual-loop antenna with enhanced gain is proposed in this letter. It comprises a symmetrical dual-loop radiator, a pair of magneto-electric feeding structures, two metal shorting pins, and a metal ground plane. To simultaneously broaden its impedance bandwidth and improve its radiation patterns, a symmetrical dual-loop radiator excited by a pair of magneto-electric feeding structures is introduced. By loading shorting pins between the radiator and the ground plane, the peak gain can be significantly enhanced over the band of operation. Measured results demonstrated that the proposed antenna has a wide impedance bandwidth of 48.2% ranging from 1.68 to 2.76 GHz for VSWR < 1.5 and a stable gain of 10.0 ± 0.85 dBi. Moreover, stable radiation patterns and low cross polarization are obtained in the desired frequency band. Due to these performances aforementioned, the proposed antenna is very suitable for base station applications.
Style Finder : Fine-Grained Clothing Style Recognition and Retrieval

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel finegrained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women’s Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.
Fast and robust Earth Mover's Distances

We present a new algorithm for a robust family of Earth Mover's Distances - EMDs with thresholded ground distances. The algorithm transforms the flow-network of the EMD so that the number of edges is reduced by an order of magnitude. As a result, we compute the EMD by an order of magnitude faster than the original algorithm, which makes it possible to compute the EMD on large histograms and databases. In addition, we show that EMDs with thresholded ground distances have many desirable properties. First, they correspond to the way humans perceive distances. Second, they are robust to outlier noise and quantization effects. Third, they are metrics. Finally, experimental results on image retrieval show that thresholding the ground distance of the EMD improves both accuracy and speed.
Genomics, gene expression and DNA arrays

Experimental genomics in combination with the growing body of sequence information promise to revolutionize the way cells and cellular processes are studied. Information on genomic sequence can be used experimentally with high-density DNA arrays that allow complex mixtures of RNA and DNA to be interrogated in a parallel and quantitative fashion. DNA arrays can be used for many different purposes, most prominently to measure levels of gene expression (messenger RNA abundance) for tens of thousands of genes simultaneously. Measurements of gene expression and other applications of arrays embody much of what is implied by the term ‘genomics’; they are broad in scope, large in scale, and take advantage of all available sequence information for experimental design and data interpretation in pursuit of biological understanding.
Vehicle license plate detection and recognition using non-blind image de-blurring algorithm

This paper proposes the method of vehicle license plate recognition, which is essential in the field of intelligent transportation system. The purpose of the study is to present a simple and effective vehicle license plate detection and recognition using non-bling image de-blurring algorithm. The sharpness of the edges in an image is restored by the prior information on images. The blue kernel is free of noise while using the non-blind image de-blurring algorithm. This non-blind image de-blurring (NBID) algorithm is involved in the process of removing the optimization difficulties with respect to unknown image and unknown blur. Estimation is carried out for the length of the motion kernel with Radon transform in Fourier domain. The proposed algorithm was tested on different vehicle images and achieved satisfactory results in license plate detection. The experimental results deal with the efficiency and robustness of the proposed algorithm in both synthesized and real images.
Anticipatory affect: neural correlates and consequences for choice.

'Anticipatory affect' refers to emotional states that people experience while anticipating significant outcomes. Historically, technical limitations have made it difficult to determine whether anticipatory affect influences subsequent choice. Recent advances in the spatio-temporal resolution of functional magnetic resonance imaging, however, now allow researchers to visualize changes in neural activity seconds before choice occurs. We review evidence that activation in specific brain circuits changes during anticipation of monetary incentives, that this activation correlates with affective experience and that activity in these circuits may influence subsequent choice. Specifically, an activation likelihood estimate meta-analysis of cued response studies indicates that nucleus accumbens (NAcc) activation increases during gain anticipation relative to loss anticipation, while anterior insula activation increases during both loss and gain anticipation. Additionally, anticipatory NAcc activation correlates with self-reported positive arousal, whereas anterior insula activation correlates with both self-reported negative and positive arousal. Finally, NAcc activation precedes the purchase of desirable products and choice of high-risk gambles, whereas anterior insula activation precedes the rejection of overpriced products and choice of low-risk gambles. Together, these findings support a neurally plausible framework for understanding how anticipatory affect can influence choice.
Solving 0-1 knapsack problems based on amoeboid organism algorithm

The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model.
Intelligent Navigation Systems for Building Evacuation

Intelligent navigation systems can significantly improve the evacuation of civilians in urban emergencies by providing dynamic directions to the evacuees as the hazard spreads. In this paper, we propose two distributed and adaptive systems for building evacuation. The first system, called intelligent evacuation system (IES), is composed of a wireless network of static decision nodes (DNs) installed in the building which provide movement decision support to evacuees by directing them to exits via less hazardous routes. The second system, called opportunistic communications based evacuation system (OCES), is based on mobile communication nodes (CNs) carried by civilians, which form an opportunistic network to exchange information on the situation and provide directions to civilians. We evaluate our systems’ performance with simulation experiments of a three-floor office building. Our results indicate that both systems can greatly improve the outcome of an emergency.
Indistinguishability of Random Systems

An (X ,Y)-random system takes inputs X1, X2, . . . ∈ X and generates, for each new input Xi, an output Yi ∈ Y, depending probabilistically on X1, . . . , Xi and Y1, . . . , Yi−1. Many cryptographic systems like block ciphers, MAC-schemes, pseudo-random functions, etc., can be modeled as random systems, where in fact Yi often depends only on Xi, i.e., the system is stateless. The security proof of such a system (e.g. a block cipher) amounts to showing that it is indistinguishable from a certain perfect system (e.g. a random permutation). We propose a general framework for proving the indistinguishability of two random systems, based on the concept of the equivalence of two systems, conditioned on certain events. This abstraction demonstrates the common denominator among many security proofs in the literature, allows to unify, simplify, generalize, and in some cases strengthen them, and opens the door to proving new indistinguishability results. We also propose the previously implicit concept of quasi-randomness and give an efficient construction of a quasi-random function which can be used as a building block in cryptographic systems based on pseudorandom functions.