nils-herrmann/clean_scirepeval huggingface dataset:
Frozen Suit: Designing a Changeable Stiffness Suit and its Application to Haptic Games

We present the concept of Frozen Suit, a type of clothing that restricts users' movements at joint positions (e.g. elbow, knee) via a changeable stiffness jamming material. The suit can "freeze" users' body parts, for example during a game in order to provide the physical sensation of being frozen by an enemy. In this paper we first present the Frozen Suit concept and its potential applications. We then systematically investigate how to design jamming patches in order to sufficiently restrict an arm or a leg. In particular we used low-fidelity prototypes to explore the restricting power of different material and particles. In order to push this analysis further we conducted a controlled experiment in order to compare the perceived stiffness of different patches sizes attached to the elbow. We performed a paired comparison experience and used a Bradley-Terry-Luce model to analyze the subjective feedback from participants. We found that 20cm long x 7cm large is the most restrictive patch and that an increase in patch area correlates with an increase in perceived stiffness (quadratic). We finish by presenting a use case application with a game that we implemented where enemies can freeze the player.
Feeling and believing: the influence of emotion on trust.

The authors report results from 5 experiments that describe the influence of emotional states on trust. They found that incidental emotions significantly influence trust in unrelated settings. Happiness and gratitude--emotions with positive valence--increase trust, and anger--an emotion with negative valence--decreases trust. Specifically, they found that emotions characterized by other-person control (anger and gratitude) and weak control appraisals (happiness) influence trust significantly more than emotions characterized by personal control (pride and guilt) or situational control (sadness). These findings suggest that emotions are more likely to be misattributed when the appraisals of the emotion are consistent with the judgment task than when the appraisals of the emotion are inconsistent with the judgment task. Emotions do not influence trust when individuals are aware of the source of their emotions or when individuals are very familiar with the trustee.
Pseudo-CMOS: A Design Style for Low-Cost and Robust Flexible Electronics

Thin-film transistors (TFTs) are a key element of flexible electronics implemented on low-cost substrates. Most TFT technologies, however, have only monotype-either n- or p-type-devices. In this paper, we propose a novel design style Pseudo-CMOS for flexible electronics that uses only monotype single-VT TFTs but has comparable performance with the complementary-type or dual-VT designs. The manufacturing cost and complexity can therefore be significantly reduced, whereas the circuit yield and reliability are enhanced with built-in postfabrication tunability. Digital cells are fabricated in two different TFT technologies, i.e., p-type self-assembled-monolayer-organic TFTs and n-type metal-oxide InGaZnO TFTs, to validate the proposed Pseudo-CMOS design style. To the best of our knowledge, this is the first design solution that has been experimentally proven to achieve superior performance for both types of TFT technologies.
Compact second harmonic-suppressed bandstop and bandpass filters using open stubs

Integration of bandstop filters with the bandstop or bandpass filter are presented in this paper. By replacing the series quarter-wavelength connecting lines of conventional open-stub bandpass/bandstop filters with the equivalent T-shaped lines, one could have compact open-stub bandstop/bandpass filters with second harmonic suppression. Transmission-line model calculation is used to derive the design equations of the equivalent T-shaped lines. Experiments have also been done to validate the design concept. Compared with the conventional open-stub bandpass/bandstop filters, over 30-dB improvement of the second harmonic suppression and 28.6% size reduction are achieved
Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.
Development of thai question answering system

Automatic question answering systems play an important role nowadays. In this paper, we developed a Thai question answering system. From an input question, the system analyzes an expected type of answer (e.g. human, place) then searches for all possible answers from source documents. In order to select the most relevance answer, a word order consistency and a relevance score are proposed to measure similarity of the question and the sentences surrounding the possible answers. Based on an experiment from 210 questions and 50 articles, the proposed system can provide a mean reciprocal answer rank of 0.657.
A Multiband CPW-Fed Slot Antenna with Fractal Stub and Parasitic Line

This paper presents a multiband CPW-fed slot antenna with fractal stub and parasitic line. The conventional wideband slot antenna with fractal stub is modified by inserting the parasitic line surrounding the fractal stub that affects the attribution to be a multiband operation suitable for some applications in wireless communication systems. The parasitic line surrounding the fractal stub can generate a dual-notched frequency that can be controlled by varying the parameters of the parasitic structure. The lengths of slit and stub on both sides of the parasitic line can control the lower and higher notched frequencies, respectively. Additionally, the prototype of the proposed antenna can operate and cover the applications of DCS 1800, WiMAX IEEE 802.16, WLAN IEEE 802.11a/b/g, and IMT advance systems.
Conversion of urdu nastaliq to roman urdu using OCR

This paper deals with Urdu Nastaliq, which is a popular script of writing Urdu language. The complex and cursive nature of nastaliq makes optical character recognition for this script very challenging. Segmentation of urdu nastaliq is also very complex due to different levels and shapes of characters according to their position in a word. Character based segmentation technique for urdu names has been proposed in this paper. After segmenting each character, the characters are matched to their templates already saved in the database. The proposed technique handles many complexities in conversion of urdu names to roman urdu including vowel sounds produced by a single character due to diacritics and different voices of ye, ain and other such characters. At the end names written in nastaliq script are converted into roman urdu.
Clinical decision support for Alzheimer's disease based on deep learning and brain network

Modern e-health systems have undergone rapid development thanks to the advances in communications, computing and machine learning technology. Especially, deep learning has great superiority in image analysis and disease prediction. In this paper, we use Alzheimer's Disease (AD) as an example to show advantages of deep learning in diagnosing brain diseases and providing clinical decision support. Firstly, we convert raw functional magnetic resonance imaging (fMRI) to a matrix to represent activity of 90 brain regions. Secondly, to represent the functional connectivity between different brain regions, a correlation matrix is obtained by calculating the correlation between each pair of brain regions. In the next, a targeted autoencoder network is built to classify the correlation matrix, which is sensitive to AD. Finally, the experiment results show that our proposed method for AD prediction achieves much better effects than traditional means. It finds the correlations between different brain regions efficiently, provides strong reference for AD prediction. Compared to Support Vector Machine (SVM), about 25% improvement is gained in prediction accuracy. The e-health field becomes more complete and effective owing to that. Our work helps predict AD at an early stage and take measures to slow down or even prevent the onset of it.
Decision feedback equalization

As real world communication channels are stressed with higher data rates, intersymbol interference (ISI) becomes a dominant limiting factor. One way to combat this effect that has recently received considerable attention is the use of a decision feedback equalizer (DFE) in the receiver. The action of the DFE is to feed back a weighted sum of past decision to cancel the ISI they cause in the present signaling interval. This paper summarizes the work in this area beginning with the linear equalizer. Three performance criteria have been used to derive optimum systems; 1) minimize the noise variance under a "zero forcing" (ZF) constraint i.e., insist that all intersymbol interference is cancelled, 2) minimize the mean-square error (MMSE) between the true sample and the observed signal just prior to the decision threshold, and 3) minimize the probability of error (Min Pe). The transmitter can be fixed and the receiver optimized or one can obtain the joint optimum transmitter and receiver. The number of past decisions used in the feedback equalization can be finite or infinite. The infinite case is easier to handle analytically. In addition to reviewing the work done in the area, we show that the linear equalizer is in fact a portion of the DFE receiver and that the processing done by the DFE is exactly equivalent to the general problem of linear prediction. Other similarities in the various system structures are also shown. The effect of error propagation due to incorrect decisions is discussed, and the coaxial cable channel is used as an example to demonstrate the improvement available using DFE.
Deep Neural Network for Learning to Rank Query-Text Pairs

This paper considers the problem of document ranking in information retrieval systems by Learning to Rank. We propose ConvRankNet combining a Siamese Convolutional Neural Network encoder and the RankNet ranking model which could be trained in an end-to-end fashion. We prove a general result justifying the linear test-time complexity of pairwise Learning to Rank approach. Experiments on the OHSUMED dataset show that ConvRankNet outperforms systematically existing feature-based models.
Common Mode EMI Noise Suppression for Bridgeless PFC Converters

The goal of this paper is to study the possibility of minimizing common mode (CM) noise emission in bridgeless power factor correction (PFC) converters. Two approaches are proposed. In the first approach, the bridgeless PFC is modified to achieve symmetry. A CM noise model for symmetric topology is derived and the conditions for symmetry are summarized. Parasitics critical to the symmetrical condition are studied and carefully controlled. As a result, CM noise can be minimized with good cancellation. The second approach is to introduce a balance technique to bridgeless PFC converters. The topology is modified so that the balance technique can be applied so as to minimize CM noise. Experimental results validate that both approaches can greatly reduce CM noise up to 30 dBmuV. The two approaches are compared in terms of both its effects on CM noise and their implementations.
Customer relationship management capabilities Measurement , antecedents and consequences

This study seeks to extend the resource-based view to the context of customer relationship management. It is intended to develop a measurement model of customer relationship management (CRM) capabilities, and to explore the key antecedents and performance consequences of CRM capabilities.  Questionnaire survey was used to collect data. In order to develop a reliable and valid measurement model of CRM capabilities, several rounds of questionnaire survey were conducted, and hypotheses were tested by utilizing the technique of structural equation modeling.  A three-factor (customer interaction management capability, customer relationship upgrading capability and customer win-back capability) measurement model of CRM capabilities is developed and tested. Furthermore, results support the hypothesized influences of customer orientation, customer-centric organizational system and CRM technology on CRM capabilities, as well as the influence of CRM capabilities on organizational performance.  This study provides a useful measurement mode of CRM capabilities that managers can use to evaluate the status in quo of CRM capabilities of their firms. Managers may also improve their CRM programs more effectively and efficiently by deploying such strategic resources of firms as customer orientation, customer-centric organizational system and CRM technology to build and strengthen their CRM capabilities.  The paper addresses significant gaps in the current literature by taking a capability view of CRM, developing a valid measurement model of CRM capabilities, and examining how possession of important CRM resources influences the effective deployment of CRM capabilities.
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora

A significant portion of the world’s text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA’s latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA’s improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets.
Research Report: Empirical Test of an EDI Adoption Model

This paper is the first test of a parsimonious model that posits three factors as determinants of the adoption of electronic data interchange (EDI): readiness, perceived benefits, and external pressure. To construct the model, we identified and organized the factors that were found to be influential in prior EDI research. By testing all these factors together in one model, we are able to investigate their relative contributions to EDI adoption decisions. Senior purchasing managers, chosen for their experience with EDI and proximity to the EDI adoption decision, were surveyed and their responses analyzed using structural equation modeling. All three determinants were found to be significant predictors of intent to adopt EDI, with external pressure and readiness being considerably more important than perceived benefits. We show that the constructs in this model can be categorized into three levels: technological, organizational, and interorganizational. We hypothesize that these categories of influence will continue to be determinants of the adoption of other emerging forms of interorganizational systems (IOS).
Exploiting Trade-offs in Symbolic Execution for Identifying Security Bugs

Over the past 20 years, our society has become increasingly dependent on software. Today, we rely on software for our financial transactions, our work, our communications, even our social contacts. A single software flaw is enough to cause irreparable damage, and as our reliance on software increases, so does our need for developing systematic techniques that check the software we use for critical vulnerabilities. In this dissertation, we investigate trade-offs in symbolic execution for identifying security-critical bugs. In the first part of the dissertation, we present symbolic execution systems capable of demonstrating control flow hijacks on real-world programs both at the source, and binary level. By exploiting specific trade-offs in symbolic execution, such as state pruning and careful state modeling, we show how to increase the efficacy of vanilla symbolic execution in identifying exploitable bugs. In the second part of the dissertation, we investigate veritesting, a symbolic execution technique for exploiting the trade-off between formula expressivity and number of program states. Our experiments on a large number of programs, show that veritesting finds more bugs, obtains higher node and path coverage, and can cover a fixed number of paths faster when compared to vanilla symbolic execution. Using veritesting, we have checked more than 33,248 Debian binaries, and found more than 11,687 bugs. Our results have had real world impact with 202 bug fixes already present in the latest version of Debian.
Blocked recursive image composition with exclusion zones

Photo collages are a popular and powerful storytelling mechanism. They are often enhanced with background artwork that sets the theme for the story. However, layout algorithms for photo collage creation typically do not take this artwork into account, which can result in collages where photos overlay important artwork elements. To address this, we extend our previous Blocked Recursive Image Composition (BRIC) method to allow any number of photos to be automatically arranged around preexisting exclusion zones on a canvas (exBRIC). We first generate candidate binary splitting trees to partition the canvas into regions that accommodate both photos and exclusion zones. We use a Cassowary constraint solver to ensure that the desired exclusion zones are not covered by photos. Finally, photo areas, exclusion zones and layout symmetry are evaluated to select the best candidate. This method provides flexible, dynamic and integrated photo layout with background artwork.
Linking organizational resources and work engagement to employee performance and customer loyalty: the mediation of service climate.

This study examined the mediating role of service climate in the prediction of employee performance and customer loyalty. Contact employees (N=342) from 114 service units (58 hotel front desks and 56 restaurants) provided information about organizational resources, engagement, and service climate. Furthermore, customers (N=1,140) from these units provided information on employee performance and customer loyalty. Structural equation modeling analyses were consistent with a full mediation model in which organizational resources and work engagement predict service climate, which in turn predicts employee performance and then customer loyalty. Further analyses revealed a potential reciprocal effect between service climate and customer loyalty. Implications of the study are discussed, together with limitations and suggestions for future research.
GradNets: Dynamic Interpolation Between Neural Architectures

In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.
Cu pillar bumps as a lead-free drop-in replacement for solder-bumped, flip-chip interconnects

We evaluated Cu pillar bumps with a lead-free SnAg solder cap as 1st-level soldered interconnects in a standard organic BGA (ball grid array) flip chip package. Various pillar heights and solder volumes were investigated at a minimum pad pitch of 150 mum. Flip chip assembly followed the exact process as in the case of collapsible SnAg solder bumps, using an identical material set and identical equipment. Thanks to the properties of Cu pillar bumps, substrate design tolerances could be relaxed compared to solder bump interconnects of the same pitch. This, together with the fact that no solder-on-pad (SOP) is required for Cu pillar bumps, allows for lower substrate cost, which is a major factor in flip chip packaging cost. Cu pillar bumps also offer significantly higher current carrying capability and better thermal performance compared to solder bumps. We found that flip chip assembly with Cu pillar bumps is a robust process with regard to variations in assembly parameters, such as solder cap volume, flux depth, chip placement accuracy and substrate pad size. It is possible to attach the Cu pillar bumps to the metal lines of the non-SOP substrates with line spacing down to 40 mum plusmn15 mum without any solder bridging. It is shown that flip chip packages with suitable 150 mum-pitch Cu pillar bumps provide high mechanical and electrical reliability. No corrosion on the Cu pillars was found after humidity tests (MSL2 level and unbiased HAST for 192 h). Thermal cycling passed 2000 cycles, even at JEDEC JESD22-A104-B condition C (-65 degC to 150 degC). High Temperature Storage passed 2000 h at 150 degC. Cross sections reveal that after 1000 h at 150degC all Sn in the solder is transformed into Cu-Sn intermetallic compounds (IMCs). Preliminary electromigration test results at highly accelerated conditions (Tbump ap 177 degC, I = 0.8 A) show almost 2 orders of magnitude longer lifetime compared to SnAg solder bumps at 200 mum pitch. Cu pillar lifetimes at high current and temperature are expected to highly exceed those of solder bumps, because current crowding in the solder is avoided and solder is transformed into much more stable intermetallics.