nils-herrmann/clean_scirepeval huggingface dataset:
Battery Modeling for Energy-Aware System Design

M any features of modern portable electronic devices—such as high-speed processors, colorful displays, opti-cal/magnetic storage drives, and wireless network interfaces—carry a significant energy cost. However, advances in battery technology have not kept pace with rapidly growing energy demands. Most laptops, handheld PCs, and cell phones use rechargeable electrochemical batteries—typically, lithium-ion batteries—as their portable energy source. These batteries take anywhere from 1.5 to 4 hours to fully charge, but they can run on this charge for only a few hours or, in the case of some newer pocket PCs, up to about 14 hours. The battery has thus emerged as a key parameter to control in the energy management of porta-bles. 3-8 To meet the stringent power budget of these devices, researchers have explored various architectural , hardware, software, and system-level optimizations to minimize the energy consumed per useful computation. Maximizing the number of useful computations is effectively a problem of maximizing battery lifetime subject to system performance constraints. Given a load applied to a battery over a certain period, information about when the battery fails as well as its state of charge, or remaining capacity, at any time can be used to trade off system performance for battery lifetime at both the design stage and runtime, possibly with the user's active participation. For example, an energy-aware picture phone could let a user trade off image quality with talk time and the number of photos the phone could take using the remaining battery capacity. Incorporating battery-state information into a lifetime optimization strategy requires a mathematical model that captures battery nonlinearities. Accurate low-level models 9-11 based on the differential equations that describe the complex phenomena occurring in an electrochemical cell have been around for about a decade, but solving these equations can take days. In recent years, however, researchers have developed high-level battery models 4,5,7,12-15 that reduce simulation time while predicting relevant variables with acceptable accuracy. Because the energy drawn from a battery is not always equivalent to the energy consumed in device circuits, understanding discharge behavior is essential for optimal system design. Batteries consist of cells arranged in series, parallel , or a combination of both. Two electrodes—an anode and a cathode, separated by an electrolyte— constitute each cell's active material. When the cell is connected to a load, a reduction-oxidation reaction transfers electrons from the anode to the cathode. This transfer converts the chemical energy stored in the active material …
Comparative study of algorithms for Atrial Fibrillation detection

Automatic detection of Atrial Fibrillation (AF) is necessary for the long-term monitoring of patients who are suspected to have AF. Several methods for AF detection exist in the literature. These methods are mainly based on two different characteristics of AF ECGs: the irregularity of RR intervals (RRI) and the fibrillatory electrical Atrial Activity (AA). The electrical AA is characterized by the absence of the P-wave (PWA) and special frequency properties (FSA). Nine AF detection algorithms were selected from literature and evaluated with the same protocol in order to study their performance under different conditions. Results showed that the highest sensitivity (Se=97.64%) and specificity (Sp=96.08%) was achieved with methods based on analysis of irregularity of RR interval, while combining RR and atrial activity analysis gave the highest positive predictive value (PPV=92.75%). Algorithms based on RR irregularity were also the most robust against noise (Se=85.79% and Sp=81.90% for SNR=0dB; and Se=82.52% and Sp=40.47% for SNR=−5dB).
Directing exploratory search: reinforcement learning from user interactions with keywords

Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.
Real-Time Tracking of Non-Rigid Objects Using Mean Shift

A new method for r eal-time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and nds the most probable tar get p osition in the current frame. The dissimilarity between the target model (its c olor distribution) and the target candidates is expr essed by a metric derive d from the Bhattacharyya coe cient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and e cient solution. The capability of the tracker to handle in real-time partial occlusions, signi cant clutter, and target scale variations, is demonstrated for several image sequences.
Transcriptional regulatory networks in Saccharomyces cerevisiae.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
Design Principles for Industrie 4.0 Scenarios

The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios.
A novel algorithm for color constancy

Color constancy is the skill by which it is possible to tell the color of an object even under a colored light. I interpret the color of an object as its color under a fixed canonical light, rather than as a surface reflectance function. This leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible. In this framework, color constancy requires estimating the illuminant under which the image was taken. The estimate is then used to choose one of a set of linear maps, which is applied to the image to yield a color descriptor at each point. This set of maps is computed in advance. The illuminant can be estimated using image measurements alone, because, given a number of weak assumptions detailed in the text, the color of the illuminant is constrained by the colors observed in the image. This constraint arises from the fact that surfaces can reflect no more light than is cast on them. For example, if one observes a patch that excites the red receptor strongly, the illuminant cannot have been deep blue. Two algorithms are possible using this constraint, corresponding to different assumptions about the world. The first algorithm, Crule will work for any surface reflectance. Crule corresponds to a form of coefficient rule, but obtains the coefficients by using constraints on illuminant color. The set of illuminants for which Crule will be successful depends strongly on the choice of photoreceptors: for narrowband photoreceptors, Crule will work in an unrestricted world. The second algorithm, Mwext, requires that both surface reflectances and illuminants be chosen from finite dimensional spaces; but under these restrictive conditions it can recover a large number of parameters in the illuminant, and is not an attractive model of human color constancy. Crule has been tested on real images of Mondriaans, and works well. I show results for Crule and for the Retinex algorithm of Land (Land 1971; Land 1983; Land 1985) operating on a number of real images. The experimental work shows that for good constancy, a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans.
Learning Syntactic Patterns for Automatic Hypernym Discovery

We present a new algorithm for learning hypernym (is-a) relations from text, a key problem in machine learning for natural language understanding. This method generalizes earlier work that relied on hand-built lexico-syntactic patterns by introducing a general-purpose formalization of the pattern space based on syntactic dependency paths. We learn these paths automatically by taking hypernym/hyponym word pairs from WordNet, finding sentences containing these words in a large parsed corpus, and automatically extracting these paths. These paths are then used as features in a high-dimensional representation of noun relationships. We use a logistic regression classifier based on these features for the task of corpus-based hypernym pair identification. Our classifier is shown to outperform previous pattern-based methods for identifying hypernym pairs (using WordNet as a gold standard), and is shown to outperform those methods as well as WordNet on an independent test set.
Data-driven robust optimization

The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests. The approach is flexible and widely applicable, and robust optimization problems built from our new sets are computationally tractable, both theoretically and practically. Furthermore, optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee whenever the constraints and objective function are concave in the uncertainty. We describe concrete procedures for choosing an appropriate set for a given application and applying our approach to multiple uncertain constraints. Computational evidence in portfolio management and queueing confirm that our data-driven sets significantly outperform traditional robust optimization techniques whenever data are available.
An efficient FPGA overlay for portable custom instruction set extensions

Custom instruction set extensions can substantially boost performance of reconfigurable softcore CPUs. While this approach is commonly tailored to one specific FPGA system, we are presenting a fine-grained FPGA-like overlay architecture which can be implemented in the user logic of various FPGA families from different vendors. This allows the execution of a portable application consisting of a program binary and an overlay configuration in a completely heterogeneous environment. Furthermore, we are presenting different optimizations for dramatically reducing the implementation cost of the proposed overlay architecture. In particular, this includes the mapping of the overlay interconnection network directly into the switch fabric of the hosting FPGA. Our case study demonstrates an overhead reduction of an order of magnitude as compared to related approaches.
MOVIE RECOMMENDATION SYSTEM BASED ON USERS ’ SIMILARITY

The growth of e-Commerce has given the birth to recommendation engine. There are many recommendation engines existing in the market to recommend different stuffs to the users. These recommendations are based on different aspects such as interest of users, history of users, location of users and many more. In all the above aspects, one thing is common and that is individuality. The engine recommends users on the basis of users’ perspective; but there are things in market which are worth concerned and that a user is unaware of. These things must also recommended to the users by the engine; but due to the limitation of ”individuality”, these engines do not recommend things that are out of the box. The hybrid movie recommendation engine has overcome this limitation of individuality. The engine will recommend movies to the users as per their interest as well as it will recommend movies rated by other users who are similar to the user. In addition to that, there are web services which will act as an adornment to the application.
Monitoring latency with OpenFlow

Software Defined Networking, especially through protocols like OpenFlow, is becoming more and more present in networks. It aims at separating the data plane from the control plane for more network programmability, serviceability, heterogeneity and maintainability. Even if mobile applications and multimedia are often pointed at to show the demise of current network architectures, there are currently no ways to efficiently dynamically obtain the latency in an OpenFlow network to efficiently apply QoS policies. In this paper, we propose a mechanism to measure link latencies from an OpenFlow controller with high accuracy and a low footprint. We implemented it and present the performance evaluation. A monitoring packet consumes only 24 Bytes, which is 81% less than the ping utility, for an average accuracy of 99.25% compared to the ping values.
The psychological functions of music listening

Why do people listen to music? Over the past several decades, scholars have proposed numerous functions that listening to music might fulfill. However, different theoretical approaches, different methods, and different samples have left a heterogeneous picture regarding the number and nature of musical functions. Moreover, there remains no agreement about the underlying dimensions of these functions. Part one of the paper reviews the research contributions that have explicitly referred to musical functions. It is concluded that a comprehensive investigation addressing the basic dimensions underlying the plethora of functions of music listening is warranted. Part two of the paper presents an empirical investigation of hundreds of functions that could be extracted from the reviewed contributions. These functions were distilled to 129 non-redundant functions that were then rated by 834 respondents. Principal component analysis suggested three distinct underlying dimensions: People listen to music to regulate arousal and mood, to achieve self-awareness, and as an expression of social relatedness. The first and second dimensions were judged to be much more important than the third-a result that contrasts with the idea that music has evolved primarily as a means for social cohesion and communication. The implications of these results are discussed in light of theories on the origin and the functionality of music listening and also for the application of musical stimuli in all areas of psychology and for research in music cognition.
Web Mining Research: A Survey

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
Extreme visualization: squeezing a billion records into a million pixels

Database searches are usually performed with query languages and form fill in templates, with results displayed in tabular lists. However, excitement is building around dynamic queries sliders and other graphical selectors for query specification, with results displayed by information visualization techniques. These filtering techniques have proven to be effective for many tasks in which visual presentations enable discovery of relationships, clusters, outliers, gaps, and other patterns. Scaling visual presentations from millions to billions of records will require collaborative research efforts in information visualization and database management to enable rapid aggregation, meaningful coordinated windows, and effective summary graphics. This paper describes current and proposed solutions (atomic, aggregated, and density plots) that facilitate sense-making for interactive visual exploration of billion record data sets.
Game AI revisited

More than a decade after the early research efforts on the use of artificial intelligence (AI) in computer games and the establishment of a new AI domain the term ``game AI'' needs to be redefined. Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation and pathfinding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would significantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a significant potential for the design of better games.
 This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI field and evidently put the game AI term under a new perspective. These game AI flagship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities.
Text2Scene: Generating Abstract Scenes from Textual Descriptions

In this paper, we propose an end-to-end model that learns to interpret natural language describing a scene to generate an abstract pictorial representation. The pictorial representations generated by our model comprise the spatial distribution and attributes of the objects in the described scene. Our model uses a sequence-to-sequence network with a double attentive mechanism and introduces a regularization strategy. These scene representations can be sampled from our model similarly as in languagegeneration models. We show that the proposed model, initially designed to handle the generation of cartoon-like pictorial representations in the Abstract Scenes Dataset, can also handle, under minimal modifications, the generation of semantic layouts corresponding to real images in the COCO dataset. Human evaluations using a visual entailment task show that pictorial representations generated with our full model can entail at least one out of three input visual descriptions 94% of the times, and at least two out of three 62% of the times for each image.
Harmonic Resonance Evaluation for Hub Traction Substation Consisting of Multiple High-Speed Railways

In many hub traction power-supply systems (TPSSs), the 27.5 kV busbars in the traction substation (TSS) have supplied several railway lines in a local area for reducing the number of TSSs. However, the multiple railway lines connected to the busbar may affect the resonance behaviors of the entire system. It is therefore necessary to investigate the resonance behaviors under such hub TPSS conditions since the harmonic current collection of all railway lines will aggravate the resonance problem. Thus, resonance-mode assessment and its sensitivity method is adopted for investigating the resonance behaviors of hub TPSS affected by different numbers of connected lines, lengths of supply lines, system capacities, etc. The numerical results of modal sensitivity indices are compared to investigate and quantify the harmonic resonance issue caused by multiple electrical railway lines. In addition, the resonance frequency-shift technique and modal impedance sensitivity index-based method are also employed to mitigate the critical harmonic resonances. The results illustrate that it is effective to analyze resonance issues by adopting the harmonic model and modal methods in this paper. The two types of resonance behaviors (i.e., primary resonance and resonance band) caused from the hub TPSS have been fully investigated, and the resulting harmonic distortion of hub TPSS is suppressed.
Low-Power Near-Threshold 10T SRAM Bit Cells With Enhanced Data-Independent Read Port Leakage for Array Augmentation in 32-nm CMOS

The conventional six-transistor static random access memory (SRAM) cell allows high density and fast differential sensing but suffers from half-select and read-disturb issues. Although the conventional eight-transistor SRAM cell solves the read-disturb issue, it still suffers from low array efficiency due to deterioration of read bit-line (RBL) swing and  $\text{I}_{\mathbf {on}}/\text{I}_{\mathbf {off}}$  ratio with increase in the number of cells per column. Previous approaches to solve these issues have been afflicted by low performance, data-dependent leakage, large area, and high energy per access. Therefore, in this paper, we present three iterations of SRAM bit cells with nMOS-only based read ports aimed to greatly reduce data-dependent read port leakage to enable 1k cells/RBL, improve read performance, and reduce area and power over conventional and 10T cell-based works. We compare the proposed work with other works by recording metrics from the simulation of a 128-kb SRAM constructed with divided-wordline-decoding architecture and a 32-bit word size. Apart from large improvements observed over conventional cells, up to 100-mV improvement in read-access performance, up to 19.8% saving in energy per access, and up to 19.5% saving in the area are also observed over other 10T cells, thereby enlarging the design and application gamut for memory designers in low-power sensors and battery-enabled devices.