nils-herrmann/clean_scirepeval huggingface dataset:
Non-transitive Hashing with Latent Similarity Components

Approximating the semantic similarity between entities in the learned Hamming space is the key for supervised hashing techniques. The semantic similarities between entities are often non-transitive since they could share different latent similarity components. For example, in social networks, we connect with people for various reasons, such as sharing common interests, working in the same company, being alumni and so on. Obviously, these social connections are non-transitive if people are connected due to different reasons. However, existing supervised hashing methods treat the pairwise similarity relationships in a simple and unified way and project data into a single Hamming space, while neglecting that the non-transitive property cannot be ade- quately captured by a single Hamming space. In this paper, we propose a non-transitive hashing method, namely Multi-Component Hashing (MuCH), to identify the latent similarity components to cope with the non-transitive similarity relationships. MuCH generates multiple hash tables with each hash table corresponding to a similarity component, and preserves the non-transitive similarities in different hash table respectively. Moreover, we propose a similarity measure, called Multi-Component Similarity, aggregating Hamming similarities in multiple hash tables to capture the non-transitive property of semantic similarity. We conduct extensive experiments on one synthetic dataset and two public real-world datasets (i.e. DBLP and NUS-WIDE). The results clearly demonstrate that the proposed MuCH method significantly outperforms the state-of-art hashing methods especially on search efficiency.
Deferred Lightweight Indexing for Log-Structured Key-Value Stores

The recent shift towards write-intensive workload on big data (e.g., financial trading, social user-generated data streams)has pushed the proliferation of log-structured key-value stores, represented by Google's BigTable , Apache HBase  andCassandra . While providing key-based data access with aPut/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in modern web and database applications. In this paper, we present DELI, a DEferred Lightweight Indexing scheme on the log-structured key-value stores. To index intensively updated bigdata in real time, DELI aims at making the index maintenance as lightweight as possible. The key idea is to apply an append-only design for online index maintenance and to collect index garbage at carefully chosen time. DELI optimizes the performance of index garbage collection through tightly coupling its execution with a native routine process called compaction. The DELI's system design is fault-tolerant and generic (to most key-valuestores), we implemented a prototype of DELI based on HBase without internal code modification. Our experiments show that the DELI offers significant performance advantage for the write-intensive index maintenance.
Creating user-mode device drivers with a proxy

Writing Windows NT device drivers can be a daunting task. Device drivers must be fully re-entrant, must use only limited resources and must be created with special development environments. Executing device drivers in user-mode offers significant coding advantages. User-mode device drivers have access to all user-mode libraries and applications. They can be developed using standard development tools and debugged on a single machine. Using the Proxy Driver to retrieve I/O requests from the kernel, user-mode drivers can export full device services to the kernel and applications. User-mode device drivers offer enormous flexibility for emulating devices and experimenting with new file systems. Experimental results show that in many cases, the overhead of moving to user-mode for processing I/O can be masked by the inherent costs of accessing physical devices.
Continuous Authentication on Mobile Devices by Analysis of Typing Motion Behavior

Smartphones have become the standard personal device to store private or sensitive information. Widely used as every day gadget, however, they are susceptible to get lost or stolen. To protect information on a smartphone from being physically accessed by attackers, a lot of authentication methods have been proposed in recent years. Each one of them suffers from certain drawbacks, either they are easy to circumvent, vulnerable against shoulder surfing attacks, or cumbersome to use. In this paper, we present an alternative approach for user authentication that is based on the smartphone’s sensors. By making use of the user’s biometrical behavior while entering text into the smartphone, we transparently authenticate the user in an ongoing-fashion. In a field study, we asked more than 300 participants to enter some short sentences into a smartphone while all available sensor events were recorded to determine a typing motion fingerprint of the user. After the proper feature extraction, a machine learning classifier based on Support Vector Machines (SVM) is used to identify the authorized user. The results of our study are twofold: While our approach is able to continuously authenticate some users with high precision, there also exist participants for which no accurate motion fingerprint can be learned. We analyze these difference in detail and provide guidelines for similar problems.
The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: a supplement to the NIMH multimodal treatment study of AD/HD.

Impulsivity is a primary symptom of the combined type of Attention Deficit/Hyperactivity Disorder (AD/HD). The Stop Signal Paradigm is premised upon a primary deficit in inhibitory control in AD/HD, whereas the Delay Aversion Hypothesis, by contrast, conceptualizes impulsivity in AD/HD, not as an inability to inhibit a response, but rather as a choice to avoid delay. This study compared the ecological validity of the Stop Signal Task (SST) and Choice-Delay Task (C-DT) measure of delay aversion, with respect to their relative utility in discriminating AD/HD children from normal control participants, and their correlations with classroom observations and with ratings of impulsivity and other core AD/HD symptoms on the Conners and SNAP-IV checklists. The tasks exhibited modest discriminant validity when used individually and excellent discriminant validity when used in combination. The C-DT correlated with teacher ratings of impulsivity, hyperactivity, and conduct problems, and with observations of gross motor activity, physical aggression, and an AD/HD composite score. The SST correlated with the observations only. These results suggest that delay aversion is associated with a broad range of AD/HD characteristics whereas inhibitory failure seems to tap a more discrete dimension of executive control.
Loosely Coupled Traceability for ATL

In the context of model transformation, traceability information may be used in a wide variety of scenarios. Each of them potentially requires a different format or complexity level. Moreover, a single transformation program can be used in several contexts. Consequently, such a program may need to be able to generate different kinds of traceability information. This work aims at showing how traceability can be added to programs written in ATLAS Transformation Language while limiting dependencies to program logic. Model transformation is used to implement this approach.
Entity-centric topic-oriented opinion summarization in twitter

Microblogging services, such as Twitter, have become popular channels for people to express their opinions towards a broad range of topics. Twitter generates a huge volume of instant messages (i.e. tweets) carrying users' sentiments and attitudes every minute, which both necessitates automatic opinion summarization and poses great challenges to the summarization system. In this paper, we study the problem of opinion summarization for entities, such as celebrities and brands, in Twitter. We propose an entity-centric topic-based opinion summarization framework, which aims to produce opinion summaries in accordance with topics and remarkably emphasizing the insight behind the opinions. To this end, we first mine topics from #hashtags, the human-annotated semantic tags in tweets. We integrate the #hashtags as weakly supervised information into topic modeling algorithms to obtain better interpretation and representation for calculating the similarity among them, and adopt Affinity Propagation algorithm to group #hashtags into coherent topics. Subsequently, we use templates generalized from paraphrasing to identify tweets with deep insights, which reveal reasons, express demands or reflect viewpoints. Afterwards, we develop a target (i.e. entity) dependent sentiment classification approach to identifying the opinion towards a given target (i.e. entity) of tweets. Finally, the opinion summary is generated through integrating information from dimensions of topic, opinion and insight, as well as other factors (e.g. topic relevancy, redundancy and language styles) in an unified optimization framework. We conduct extensive experiments on a real-life data set to evaluate the performance of individual opinion summarization modules as well as the quality of the produced summary. The promising experiment results show the effectiveness of the proposed framework and algorithms.
The Gameplay Gestalt, Narrative, and Interactive Storytelling

This paper discusses the relationship between concepts of narrative, patterns of interaction within computer games constituting gameplay gestalts, and the relationship between narrative and the gameplay gestalt. The repetitive patterning involved in gameplay gestalt formation is found to undermine deep narrative immersion. The creation of stronger forms of interactive narrative in games requires the resolution of this confl ict. The paper goes on to describe the Purgatory Engine, a game engine based upon more fundamentally dramatic forms of gameplay and interaction, supporting a new game genre referred to as the fi rst-person actor. The fi rst-person actor does not involve a repetitive gestalt mode of gameplay, but defi nes gameplay in terms of character development and dramatic interaction.
Machine Learning Algorithms for Opinion Mining and Sentiment Classification

With the evolution of web technology, there is a huge amount of data present in the web for the internet users. These users not only use the available resources in the web, but also give their feedback, thus generating additional useful information. Due to overwhelming amount of user’s opinions, views, feedback and suggestions available through the web resources, it’s very much essential to explore, analyse and organize their views for better decision making.  Mining or Sentiment Analysis is a Natural Language Processing and Information Extraction task that identifies the user’s views or opinions explained in the form of positive, negative or neutral comments and quotes underlying the text. Various supervised or data-driven techniques to Sentiment analysis like Naïve Byes, Maximum Entropy and SVM.For classification use support vector machine (SVM), it performs the sentiment classification task also consider sentiment classification accuracy.
Sub 50-nm FinFET: PMOS

High performance PMOSFETs with gate length as short as 18-nm are reported. A self-aligned double-gate MOSFET structure (FinFET) is used to suppress the short channel effect. A 45 nm gate-length PMOS FinEET has an I/sub dsat/ of 410 /spl mu/A//spl mu/m (or 820 /spl mu/A//spl mu/m depending on the definition of the width of a double-gate device) at Vd=Vg=1.2 V and Tox=2.5 nm. The quasi-planar nature of this variant of the double-gate MOSFETs makes device fabrication relatively easy using the conventional planar MOSFET process technologies. Simulation shows possible scaling to 10-nm gate length.
Authorship Attribution with Latent Dirichlet Allocation

The problem of authorship attribution – attributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest in the last decade. Most of the work on authorship attribution focuses on scenarios with only a few candidate authors, but recently considered cases with tens to thousands of candidate authors were found to be much more challenging. In this paper, we propose ways of employing Latent Dirichlet Allocation in authorship attribution. We show that our approach yields state-of-the-art performance for both a few and many candidate authors, in cases where these authors wrote enough texts to be modelled effectively.
Supply and threshold voltage scaling for low power CMOS

This paper investigates the effect of lowering the supply and threshold voltages on the energy efficiency of CMOS circuits. Using a first-order model of the energy and delay of a CMOS circuit, we show that lowering the supply and threshold voltage is generally advantageous, especially when the transistors are velocity saturated and the nodes have a high activity factor. In fact, for modern submicron technologies, this simple analysis suggests optimal energy efficiency at supply voltages under 0.5 V. Other process and circuit parameters have almost no effect on this optimal operating point. If there is some uncertainty in the value of the threshold or supply voltage, however, the power advantage of this very low voltage operation diminishes. Therefore, unless active feedback is used to control the uncertainty, in the future the supply and threshold voltage will not decrease drastically, but rather will continue to scale down to maintain constant electric fields.
From Very Weak to Very Strong: Analyzing Password-Strength Meters

Millions of users are exposed to password-strength meters/checkers at highly popular web services that use userchosen passwords for authentication. Recent studies have found evidence that some meters actually guide users to choose better passwords—which is a fairly rare-bit of good news in password research. However, these meters are mostly based on ad-hoc design. At least, as we found, most vendors do not provide any explanation of their design choices, sometimes making them appear to be a black box. We analyze password meters deployed in selected popular websites, by measuring the strength labels assigned to common passwords from several password dictionaries. From this empirical analysis with millions of passwords, we report prominent characteristics of meters as deployed at popular websites. We shed light on how the server-end of some meters functions, provide examples of highly inconsistent strength outcomes for the same password in different meters, along with examples of many weak passwords being labeled as strong or even very strong. These weaknesses and inconsistencies may confuse users in choosing a stronger password, and thus may weaken the purpose of these meters. On the other hand, we believe these findings may help improve existing meters, and possibly make them an effective tool in the long run.
Synaesthesia — A Window Into Perception , Thought and Language

We investigated grapheme–colour synaesthesia and found that: (1) The induced colours led to perceptual grouping and pop-out, (2) a grapheme rendered invisible through ‘crowding’ or lateral masking induced synaesthetic colours — a form of blindsight — and (3) peripherally presented graphemes did not induce colours even when they were clearly visible. Taken collectively, these and other experiments prove conclusively that synaesthesia is a genuine perceptual phenomenon, not an effect based on memory associations from childhood or on vague metaphorical speech. We identify different subtypes of number–colour synaesthesia and propose that they are caused by hyperconnectivity between colour and number areas at different stages in processing; lower synaesthetes may have cross-wiring (or cross-activation) within the fusiform gyrus, whereas higher synaesthetes may have cross-activation in the angular gyrus. This hyperconnectivity might be caused by a genetic mutation that causes defective pruning of connections between brain maps. The mutation may further be expressed selectively (due to transcription factors) in the fusiform or angular gyri, and this may explain the existence of different forms of synaesthesia. If expressed very diffusely, there may be extensive cross-wiring between brain regions that represent abstract concepts, which would explain the link between creativity, metaphor and synaesthesia (and the higher incidence of synaesthesia among artists and poets). Also, hyperconnectivity between the sensory cortex and amygdala would explain the heightened aversion synaesthetes experience when seeing numbers printed in the ‘wrong’ colour. Lastly, kindling (induced hyperconnectivity in the temporal lobes of temporal lobe epilepsy [TLE] patients) may explain the purported higher incidence of synaesthesia in these patients . We conclude with a synaesthesia-based theory of the evolution of language. Thus, our experiments on synaesthesia and our theoretical framework attempt to link several seemingly unrelated facts about the human mind. Far from being a mere curiosity, synaesthesia may provide a window into perception, thought and language.  2001, pp. 3–34 Correspondence: Center for Brain and Cognition, University of California, San Diego, 9500 Gilman Dr. 0109, La Jolla, CA 92093-0109,
Modulation strategy to operate the dual active bridge DC-DC converter under soft switching in the whole operating range

A new modulation strategy that allows operating the dual active bridge (DAB) dc-dc converter under soft switching in the whole operating range is proposed. This strategy is ruled by imposing a certain modulation index in one of the two bridges and a phase shift between the transformer primary and secondary voltages. Moreover, the proposed algorithm reduces the reactive power and thus reducing the converter conduction losses. An experimental prototype was implemented and some experimental results are presented to validate the theoretical analysis. The experimental results reveal that the overall efficiency of the DAB topology can be improved up to 20% by implementing the proposed modulation strategy instead of the conventional one.
A two-output series-resonant inverter for induction-heating cooking appliances

Multiple-burner induction-heating cooking appliances are suitable for using multiple-output inverters. Some common approaches use several single-output inverters or a single-output inverter multiplexing the loads along the time periodically. By specifying a two-output series-resonant high-frequency inverter, a new inverter is obtained fulfilling the requirements. The synthesized converter can be considered as a two-output extension of a full-bridge topology. It allows the control of the two outputs, simultaneously and independently, up to their rated powers saving component count compared with the two-converter solution and providing a higher utilization of electronics. To verify theoretical predictions, the proposed converter is designed and tested experimentally in an induction-heating appliance prototype. A fixed-frequency control strategy is digitally implemented with good final performances for the application, including ZVS operation for active devices and a quick heating function. Although the work is focused on low-power induction heating, it can be probably useful for other power electronic applications.
Acoustic feature extraction by statistics based local binary pattern for environmental sound classification

Classification of environmental sounds is a fundamental procedure for a wide range of real-world applications. In this paper, we propose a novel acoustic feature extraction method for classifying the environmental sounds. The proposed method is motivated from the image processing technique, local binary pattern (LBP), and works on a spectrogram which forms two-dimensional (time-frequency) data like an image. Since the spectrogram contains noisy pixel values, for improving classification performance, it is crucial to extract the features which are robust to the fluctuations in pixel values. We effectively incorporate the local statistics, mean and standard deviation on local pixels, to establish robust LBP. In addition, we provide the technique of L2-Hellinger normalization which is efficiently applied to the proposed features so as to further enhance the discriminative power while increasing the robustness. In the experiments on environmental sound classification using RWCP dataset that contains 105 sound categories, the proposed method produces the superior performance (98.62%) compared to the other methods, exhibiting significant improvements over the standard LBP method as well as robustness to noise and low computation time.
The Art of Designing Socially Intelligent Agents: Science, Fiction, and the Human in the Loop

In this paper socially intelligent agents (SIA) are understood as agents which do not only from an observer point of view behave socially but which are able to recognize and identify other agents and establish and maintain relationships to other agents. The process of building socially intelligent agents is innuenced by what the human as the designer considers`social', and conversely agent tools which are behaving socially can innuence human conceptions of sociality. A Cognitive Technology (CT) approach towards designing SIA aaords as an opportunity to study the process of 1) how new forms of interactions and func-tionalities and use of technology can emerge at the human-tool interface, 2) how social agents can constrain their cognitive and social potential, and 3) how social agent technology and human (social) cognition can co-evolve and co-adapt and result in new forms of sociality. Agent-human interaction requires a cognitive t between SIA technology and the human-in-the-loop as designer of, user of, and participant in social interactions. Aspects of human social psychology, e.g. story-telling, empathy, embodiment, historical and ecological grounding can contribute to a believable and cognitively well-balanced design of SIA technology, in order to further the relationship between humans and agent tools. It is hoped that approaches to believability based on these concepts can avoid thèshallowness' that merely take advantage of the anthromorphizing tendency in humans. This approach is put into the general framework of Embodied Artiicial Life (EAL) research. The paper concludes with a terminology and list of guidelines useful for SIA design.
Meteor Universal: Language Specific Translation Evaluation for Any Target Language

Parameter set learned using all WMT12 data (Callison-Burch et al., 2012): • 100,000 binary rankings covering 8 language directions. •Restrict scoring for all languages to exact and paraphrase matching. Parameters encode human preferences that generalize across languages: •Prefer recall over precision. •Prefer word choice over word order. •Prefer correct translations of content words over function words. •Prefer exact matches over paraphrase matches, while still giving significant credit to paraphrases.