nils-herrmann/clean_scirepeval huggingface dataset:
Improving the fingerprint verification performance of set partitioning coders at low bit rates

Wavelet transform combined with the set partitioning coders (SPC) are the most widely used fingerprint image compression approach. Many different SPC coders have been proposed in the literature to encode the wavelet transform coefficients a common feature of which is trying to maximize the global peak-signal-to-noise ratio (PSNR) at a given bit rate. Unfortunately, they have not considered the local variations of SNR within the compressed fingerprint image; therefore, different regions in the compressed image will have different ridge-valley qualities. This problem causes the verification performance to be decreased because minutiae and other useful features cannot be extracted precisely from the low-bit-rate-compressed fingerprint images. Contrast variation within the original image worsens the problem. This paper deals with those applications of fingerprint image compression in which high compression ratios and preserving or improving the verification performance of the compressed images are the main concern. We propose a compression scheme in which the local-SNR (signal-to-noise ratio) variations within the compressed image are minimized (and thus, general quality is maximized everywhere) by means of an iterative procedure. The proposed procedure can be utilized in conjunction with any SPC coder without the need to modify the SPC coder’s algorithm. In addition, we used image enhancement to further improve the ridge-valley quality as well as the verification performance of the compressed fingerprint images through alleviating the leakage effect. We evaluated the compression and verification performances of some conventional and modern SPC coders including STW, EZW, SPIHT, WDR, and ASWDR combined with the proposed scheme. This evaluation was performed on the FVC2004 dataset with respect to measures including average PSNR curve versus bit rate, verification accuracy, detection error trade-off (DET) curve, and correlation of matching scores versus the average quality of involved fingerprint images. Simulation results showed considerable improvement on verification performance of all examined SPC coders, especially the SPIHT coder, by using the proposed scheme.
Transductive Multi-View Zero-Shot Learning

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/ domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.
Processing flows of information: from data stream to complex event processing

An increasing number of distributed applications requires processing continuously flowing data from geographically distributed sources at unpredictable rate to obtain timely responses to complex queries. Examples of such applications come from the most disparate fields: from wireless sensor networks to financial tickers, from traffic management to click stream inspection.
 These requirements led to the development of a number of systems specifically designed to process information as a flow according to a set of pre-deployed processing rules. We collectively call them Information Flow Processing (IFP) Systems. Despite having a common goal, IFP systems differ in a wide range of aspects, including architectures, data models, rule languages, and processing mechanisms.
 In this tutorial we draw a general framework to analyze and compare the results achieved so far in the area of IFP systems. This allows us to offer a systematic overview of the topic, favoring the communication between different communities, and highlighting a number of open issue that still need to be addressed in research.
RFID in the Retail Supply Chain

The use of RFID (radio-frequency identification) in the retail supply chain and at the point of sale (POS) holds much promise to revolutionize the process by which products pass from manufacturer to retailer to consumer. The basic idea of RFID is a tiny computer chip placed on pallets, cases, or items. The data on the chip can be read using a radio beam. RFID is a newer technology than bar codes, which are read using a laser beam. RFID is also more effective than bar codes at tracking moving objects in environments where bar code labels would be suboptimal or could not be used as no direct line of sight is available, or where information needs to be automatically updated. RFID is based on wireless (radio) systems, which allows for noncontact reading of data about products, places, times, or transactions, thereby giving retailers and manufacturers alike timely and accurate data about the flow of products through their factories, warehouses, and stores.
Energy-driven computing: Rethinking the design of energy harvesting systems

Energy harvesting computing has been gaining increasing traction over the past decade, fueled by technological developments and rising demand for autonomous and battery-free systems. Energy harvesting introduces numerous challenges to embedded systems but, arguably the greatest, is the required transition from an energy source that typically provides virtually unlimited power for a reasonable period of time until it becomes exhausted, to a power source that is highly unpredictable and dynamic (both spatially and temporally, and with a range spanning many orders of magnitude). The typical approach to overcome this is the addition of intermediate energy storage/buffering to smooth out the temporal dynamics of both power supply and consumption. This has the advantage that, if correctly sized, the system ‘looks like’ a battery-powered system; however, it also adds volume, mass, cost and complexity and, if not sized correctly, unreliability. In this paper, we consider energy-driven computing, where systems are designed from the outset to operate from an energy harvesting source. Such systems typically contain little or no additional energy storage (instead relying on tiny parasitic and decoupling capacitance), alleviating the aforementioned issues. Examples of energy-driven computing include transient systems (which power down when the supply disappears and efficiently continue execution when it returns) and power-neutral systems (which operate directly from the instantaneous power harvested, gracefully modulating their consumption and performance to match the supply). In this paper, we introduce a taxonomy of energy-driven computing, articulating how power-neutral, transient, and energy-driven systems present a different class of computing to conventional approaches.
Semi-Supervised Learning with Manifold Fitted Graphs

In this paper, we propose a locality-constrained and sparsity-encouraged manifold fitting approach, aiming at capturing the locally sparse manifold structure into neighborhood graph construction by exploiting a principled optimization model. The proposed model formulates neighborhood graph construction as a sparse coding problem with the locality constraint, therefore achieving simultaneous neighbor selection and edge weight optimization. The core idea underlying our model is to perform a sparse manifold fitting task for each data point so that close-by points lying on the same local manifold are automatically chosen to connect and meanwhile the connection weights are acquired by simple geometric reconstruction. We term the novel neighborhood graph generated by our proposed optimization model M-Fitted Graph since such a graph stems from sparse manifold fitting. To evaluate the robustness and effectiveness of M-fitted graphs, we leverage graph-based semisupervised learning as the testbed. Extensive experiments carried out on six benchmark datasets validate that the proposed M-fitted graph is superior to state-of-the-art neighborhood graphs in terms of classification accuracy using popular graph-based semi-supervised learning methods.
Process Management for Knowledge Work

In this chapter, the topic of using process improvement approaches to improve knowledge work is addressed. The effective performance of knowledge work is critical to contemporary sophisticated economies. It is suggested that traditional, engineering-based approaches to knowledge work are incompatible with the autonomy and work approaches of many knowledge workers. Therefore, a variety of alternative process-oriented approaches to knowledge work are described. Emphasis is placed on differentiating among different types of knowledge work and applying process interventions that are more behaviorally sensitive.
The Area Processing Unit of Caroline - Finding the Way through DARPA's Urban Challenge

This paper presents a vision-based color segmentation algorithm suitable for urban environments that separates an image into areas of drivable and non-drivable terrain. Assuming that a part of the image is known to be drivable terrain, other parts of the image are classified by comparing the Euclidean distance of each pixel’s color to the mean colors of the drivable area in real-time. Moving the search area depending on each frame’s result ensures temporal consistency and coherence. Furthermore, the algorithm classifies artifacts such as white and yellow lane markings and hard shadows as areas of unknown drivability. The algorithm was thoroughly tested on the autonomous vehicle ’Caroline’, which was a finalist in the 2007 DARPA Urban Challenge.
Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization

L-BFGS-B is a limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is difficult to obtain, or for large dense problems. L-BFGS-B can also be used for unconstrained problems and in this case performs similarly to its predessor, algorithm L-BFGS (Harwell routine VA15). The algorithm is implemented in Fortran 77.
Evaluation of Logic-Based Smart Contracts for Blockchain Systems

While procedural languages are commonly used to program smart contracts in blockchain systems, logic-based languages may be interesting alternatives. In this paper, we inspect what are the possible legal and technical (dis)advantages of logic-based smart contracts in light of common activities featuring ordinary contracts, then we provide insights on how to use such logic-based smart contracts in combination with blockchain systems. These insights lead us to emphasize a fundamental challenge algorithms for logic approaches have to be efficient, but they also need to be literally cheap as measured within the environment where they are deployed and according to its economic rules. We illustrate this with different algorithms from defeasible logic-based
The Dialog State Tracking Challenge Series: A Review

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation – such as the user’s goal – given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.
Effects of self-myofascial release: A systematic review.

Self-myofascial release (SMFR) is a type of myofascial release performed by the individual themselves rather than by a clinician, typically using a tool.



To review the literature regarding studies exploring acute and chronic clinical effects of SMFR.



PubMed and Google Scholar databases were searched during February 2015 for studies containing words related to the topic of SMFR.



Acutely, SMFR seems to increase flexibility and reduce muscle soreness but does not impede athletic performance. It may lead to improved arterial function, improved vascular endothelial function, and increased parasympathetic nervous system activity acutely, which could be useful in recovery. There is conflicting evidence whether SMFR can improve flexibility long-term.



SMFR appears to have a range of potentially valuable effects for both athletes and the general population, including increasing flexibility and enhancing recovery.
Scalable detection of semantic clones

Several techniques have been developed for identifying similar code fragments in programs. These similar fragments, referred to as code clones, can be used to identify redundant code, locate bugs, or gain insight into program design. Existing scalable approaches to clone detection are limited to finding program fragments that are similar only in their contiguous syntax. Other, semantics-based approaches are more resilient to differences in syntax, such as reordered statements, related statements interleaved with other unrelated statements, or the use of semantically equivalent control structures. However, none of these techniques have scaled to real world code bases. These approaches capture semantic information from Program Dependence Graphs (PDGs), program representations that encode data and control dependencies between statements and predicates. Our definition of a code clone is also based on this representation: we consider program fragments with isomorphic PDGs to be clones.
 In this paper, we present the first scalable clone detection algorithm based on this definition of semantic clones. Our insight is the reduction of the difficult graph similarity problem to a simpler tree similarity problem by mapping carefully selected PDG subgraphs to their related structured syntax. We efficiently solve the tree similarity problem to create a scalable analysis. We have implemented this algorithm in a practical tool and performed evaluations on several million-line open source projects, including the Linux kernel. Compared with previous approaches, our tool locates significantly more clones, which are often more semantically interesting than simple copied and pasted code fragments.
Machine Intelligence on Resource-Constrained IoT Devices: The Case of Thread Granularity Optimization for CNN Inference

Despite their remarkable performance in various machine intelligence tasks, the computational intensity of Convolutional Neural Networks (CNNs) has hindered their widespread utilization in resource-constrained embedded and IoT systems. To address this problem, we present a framework for synthesis of efficient CNN inference software targeting mobile SoC platforms. We argue that thread granularity can substantially impact the performance and energy dissipation of the synthesized inference software, and demonstrate that launching the maximum number of logical threads, often promoted as a guiding principle by GPGPU practitioners, does not result in an efficient implementation for mobile SoCs. We hypothesize that the runtime of a CNN layer on a particular SoC platform can be accurately estimated as a linear function of its computational complexity, which may seem counter-intuitive, as modern mobile SoCs utilize a plethora of heterogeneous architectural features and dynamic resource management policies. Consequently, we develop a principled approach and a data-driven analytical model to optimize granularity of threads during CNN software synthesis. Experimental results with several modern CNNs mapped to a commodity Android smartphone with a Snapdragon SoC show up to 2.37X speedup in application runtime, and up to 1.9X improvement in its energy dissipation compared to existing approaches.
GARNET: A detailed on-chip network model inside a full-system simulator

Until very recently, microprocessor designs were computation-centric. On-chip communication was frequently ignored. This was because of fast, single-cycle on-chip communication. The interconnect power was also insignificant compared to the transistor power. With uniprocessor designs providing diminishing returns and the advent of chip multiprocessors (CMPs) in mainstream systems, the on-chip network that connects different processing cores has become a critical part of the design. Transistor miniaturization has led to high global wire delay, and interconnect power comparable to transistor power. CMP design proposals can no longer ignore the interaction between the memory hierarchy and the interconnection network that connects various elements. This necessitates a detailed and accurate interconnection network model within a full-system evaluation framework. Ignoring the interconnect details might lead to inaccurate results when simulating a CMP architecture. It also becomes important to analyze the impact of interconnection network optimization techniques on full system behavior. In this light, we developed a detailed cycle-accurate interconnection network model (GARNET), inside the GEMS full-system simulation framework. GARNET models a classic five-stage pipelined router with virtual channel (VC) flow control. Microarchitectural details, such as flit-level input buffers, routing logic, allocators and the crossbar switch, are modeled. GARNET, along with GEMS, provides a detailed and accurate memory system timing model. To demonstrate the importance and potential impact of GARNET, we evaluate a shared and private L2 CMP with a realistic state-of-the-art interconnection network against the original GEMS simple network. The objective of the evaluation was to figure out which configuration is better for a particular workload. We show that not modeling the interconnect in detail might lead to an incorrect outcome. We also evaluate Express Virtual Channels (EVCs), an on-chip network flow control proposal, in a full-system fashion. We show that in improving on-chip network latency-throughput, EVCs do lead to better overall system runtime, however, the impact varies widely across applications.
Eye fixations and cognitive processes

A widely accepted view of the human information processing system is that most of the symbol manipulation takes place in a central processor, sometimes referred to as the active memory (Neisser, 1967), working memory (Newell & Simon, 1963), operational memory (Posner, 1967), or the immediate processor (Newell, 1973). This paper is concerned with the rapid mental operations of the central processor and how they are reflected by the pattern and duration of eye fixations during a task.involving visual input. We will examine the basic operators, parameters, and control structure of the central processor as it performs such tasks as the comparison of rotated figures (Shepard & Metzler, 1971), mental arithmetic (Parkman, 1971), sentence verification (Carpenter & Just, 1975), and memory scanning (Stemberg, 1969). These tasks generally take less than 5 or 10 set to complete, and can be decomposed into very rapid mental operations, often estimated to consume between 50 to 800 msec each. The goals of this paper are to demonstrate that the locus, duration, and sequence of the eye fixations can be closely tied to the activity of the central processor, and to exploit this relation in investigating the fine structure of the processor’s activity in a number of cognitive tasks. The primary proposal is that the eye fixates the referent of the symbol currently being processed if the referent is in view. That is, the fixation may reflect what is at the “top of the stack.” If several symbols are
Anticancer effects of gallic acid isolated from Indonesian herbal medicine, Phaleria macrocarpa (Scheff.) Boerl, on human cancer cell lines.

The natural antioxidant gallic acid (GA) was isolated from fruits of a medicinal Indonesian plant, Phaleria macrocarpa (Scheff.) Boerl. The structure was identified on the basis of spectroscopic analysis and comparison with authentic compound. GA demonstrated a significant inhibition of cell proliferation in a series of cancer cell lines and induced apoptosis in esophageal cancer cells (TE-2) but not in non-cancerous cells (CHEK-1). Observation of the molecular mechanism of apoptosis showed that GA up-regulated the pro-apoptosis protein, Bax, and induced caspase-cascade activity in cancer cells. On the other hand, GA down-regulated anti-apoptosis proteins such as Bcl-2 and Xiap. In addition, GA also induced down-regulation of the survival Akt/mTOR pathway. In non-cancerous cells, we observed delayed expression of pro-apoptosis related proteins. Our results suggest that GA might be a potential anticancer compound. However, in depth in vivo studies are needed to elucidate the exact mechanism.
Roles in Agile Software Development Teams

One of the key elements in describing a software development method is the roles that are assigned to the members of the software team. This article describes our experience in assigning roles to students who are involved in the development of software projects, working in Extreme Programming teams. This experience, which is based on 25 such projects, teaches us that a personal role for each teammate increases personal responsibility while maintaining the essence of the software development method. In this paper we discuss ways in which different software development methods address the place of roles in a software development team. We also share our experience in refining role specifications and suggest a way to achieve and measure progress by using the perspective of the different roles.
RECURRENTNEURAL NETWORK REGULARIZATION

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, t he most successful technique for regularizing neural networks, does n ot work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropo ut t LSTMs, and show that it substantially reduces overfitting on a varie ty of tasks. These tasks include language modeling, speech recognition, image capt ion generation, and machine translation.