nils-herrmann/clean_scirepeval huggingface dataset:
An 8-b 400-MS/s 2-b-Per-Cycle SAR ADC With Resistive DAC

An 8-b 400-MS/s 2-b-per-cycle (2 b/C) successive approximation register (SAR) analog-to-digital converter (ADC) is fabricated in 65-nm CMOS. With the implementation of a low-power and small-area resistive DAC and associated highly integrated circuit implementation, the proposed SAR ADC achieves rapid conversion rate, low power, and compact area, leading to SNDR of 44.5 dB and SFDR of 54.0 dB, at 400 MS/s with 1.9-MHz input. The measured FOM is 73 fJ/conversion-step at 400 MS/s from 1.2-V supply and 42 fJ/conversion-step at 250 MS/s from a 1-V supply. The active area with the digital calibration is 0.028 mm2.
The Effects of Social Popularity and Deal Scarcity at Different Stages of Online Shopping

The positive effects of social popularity (i.e., information based on other consumers’ behaviors) and deal scarcity (i.e., information provided by product vendors) on consumers’ consumption behaviors are well recognized. However, few studies have investigated their potential joint and interaction effects and how such effects may differ at different timing of a shopping process. This study examines the individual and interaction effects of social popularity and deal scarcity as well as how such effects change as consumers’ shopping goals become more concrete. The results of a laboratory experiment show that in the initial shopping stage when consumers do not have specific shopping goals, social popularity and deal scarcity information weaken each other’s effects; whereas in the later shopping stage when consumers have constructed concrete shopping goals, these two information cues reinforce each other’s effects. Implications on theory and practice are discussed.
Increasing RSSI Localization Accuracy with Distance Reference Anchor in Wireless Sensor Networks

Localization is a prominent application and research area in Wireless Sensor Networks. Various research studies have been carried out on localization techniques and algorithms in order to improve localization accuracy. Received signal strength indicator is a parameter, which has been widely used in localization algorithms in many research studies. There are several environmental and other factors that affect the localization accuracy and reliability. This study introduces a new technique to increase the localization accuracy by employing a dynamic distance reference anchor method. In order to investigate the performance improvement obtained with the proposed technique, simulation models have been developed, and results have been analyzed. The simulation results show that considerable improvement in localization accuracy can be achieved with the proposed model.
CUHK & SIAT Submission for THUMOS 15 Action Recognition Challenge

This paper presents the method of our submission for THUMOS15 action recognition challenge. We propose a new action recognition system by exploiting very deep twostream ConvNets and Fisher vector representation of iDT features. Specifically, we utilize those successful very deep architectures in images such as GoogLeNet and VGGNet to design the two-stream ConvNets. From our experiments, we see that deeper architectures obtain higher performance for spatial nets. However, for temporal net, deeper architectures could not yield better recognition accuracy. We analyze that the UCF101 dataset is relatively very small and it is very hard to train such deep networks on the current action datasets. Compared with traditional iDT features, our implemented two-stream ConvNets significantly outperform them. We further combine the recognition scores of both two-stream ConvNets and iDT features, and achieve 68% mAP value on the validation dataset of THUMOS15.
Towards Finding Optimal Differential Characteristics for ARX : Application to Salsa 20 ⋆

An increasing number of cryptographic primitives are built using the ARX operations: addition modulo 2, bit rotation and XOR. Because of their very fast performance in software, ARX ciphers are becoming increasingly common. However, there is currently no rigorous understanding of the security of ARX ciphers against one of the most common attacks in symmetric-key cryptography: differential cryptanalysis. In this paper, we introduce a tool to search for optimal differential characteristics for ARX ciphers. Our technique is very easy to use, as it only involves writing out simple equations for every addition, rotation and XOR operation in the cipher, and applying an off-the-shelf SAT solver. As is commonly done for ARX ciphers, our analysis assumes that the probability of a characteristic can be computed by multiplying the probabilities of each operation, and that the probability of the best characteristic is a good estimate for the probability of the corresponding differential. Using extensive experiments for Salsa20, we find that these assumptions are not always valid. To overcome these issues, we propose a method to accurately estimate the probability of ARX differentials.
Natural Language Assistant: A Dialog System for Online Product Recommendation

LANGUAGE ASSISTANT (NLA), a web-based natural language dialog system to help users find relevant products on electronic-commerce sites. The system brings together technologies in natural language processing and human-computer interaction to create a faster and more intuitive way of interacting with web sites. By combining statistical parsing techniques with traditional AI rule-based technology, we have created a dialog system that accommodates both customer needs and business requirements. The system is currently embedded in an application for recommending laptops and was deployed as a pilot on IBM’s web site.
Efficient algorithms for solving multiconstraint zero-one knapsack problems to optimality

The multiconstraint 0-1 knapsack problem is encountered when one has to decide how to use a knapsack with multiple resource constraints. Even though the single constraint version of this problem has received a lot of attention, the multiconstraint knapsack problem has been seldom addressed. This paper deals with developing an effective solution procedure for the multiconstraint knapsack problem. Various relaxations of the problem are suggested and theoretical relations between these relaxations are pointed out. Detailed computational experiments are carried out to compare bounds produced by these relaxations. New algorithms for obtaining surrogate bounds are developed and tested. Rules for reducing problem size are suggested and shown to be effective through computational tests. Different separation, branching and bounding rules are compared using an experimental branch and bound code. An efficient branch and bound procedure is developed, tested and compared with two previously developed optimal algorithms. Solution times with the new procedure are found to be considerably lower. This procedure can also be used as a heuristic for large problems by early termination of the search tree. This scheme was tested and found to be very effective.
Gaussian process implicit surfaces for shape estimation and grasping

The choice of an adequate object shape representation is critical for efficient grasping and robot manipulation. A good representation has to account for two requirements: it should allow uncertain sensory fusion in a probabilistic way and it should serve as a basis for efficient grasp and motion generation. We consider Gaussian process implicit surface potentials as object shape representations. Sensory observations condition the Gaussian process such that its posterior mean defines an implicit surface which becomes an estimate of the object shape. Uncertain visual, haptic and laser data can equally be fused in the same Gaussian process shape estimate. The resulting implicit surface potential can then be used directly as a basis for a reach and grasp controller, serving as an attractor for the grasp end-effectors and steering the orientation of contact points. Our proposed controller results in a smooth reach and grasp trajectory without strict separation of phases. We validate the shape estimation using Gaussian processes in a simulation on randomly sampled shapes and the grasp controller on a real robot with 7DoF arm and 7DoF hand.
Ant-based vehicle congestion avoidance system using vehicular networks

Vehicle traffic congestion leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Instead of using static algorithms, e.g. Dijkstra and A*, we present a bio-inspired algorithm, food search behavior of ants, which is a promising way of solving traffic congestion in vehicular networks. We have called this the Antbased Vehicle Congestion Avoidance System (AVCAS). AVCAS combines the average travel speed prediction of traffic on roads with map segmentation to reduce congestion as much as possible by finding the least congested shortest paths in order to avoid congestion instead of recovering from it. AVCAS collects real-time traffic data from vehicles and road side units to predict the average travel speed of roads traffic. It utilizes this information to perform an ant-based algorithm on a segmented map resulting in avoidance of congestion. Simulation results conducted on various vehicle densities show that the proposed system outperforms the existing systems in terms of average travel time, which decreased by an average of 11.5%, and average travel speed which increased by an average of 13%. In addition, AVCAS handles accident conditions in a more efficient way and decreases congestion by using alternative paths.
View Synthesis for Advanced 3D Video Systems

Interest in 3D video applications and systems is growing rapidly and technology is maturating. It is expected that multiview autostereoscopic displays will play an important role in home user environments, since they support multiuser 3D sensation and motion parallax impression. The tremendous data rate cannot be handled efficiently by representation and coding formats such as MVC or MPEG-C Part 3. Multiview video plus depth (MVD) is a new format that efficiently supports such advanced 3DV systems, but this requires high-quality intermediate view synthesis. For this, a new approach is presented that separates unreliable image regions along depth discontinuities from reliable image regions, which are treated separately and fused to the final interpolated view. In contrast to previous layered approaches, our algorithm uses two boundary layers and one reliable layer, performs image-based 3D warping only, and was generically implemented, that is, does not necessarily rely on 3D graphics support. Furthermore, different hole-filling and filtering methods are added to provide high-quality intermediate views. As a result, highquality intermediate views for an existing 9-view auto-stereoscopic display as well as other stereoand multiscopic displays are presented, which prove the suitability of our approach for advanced 3DV systems.
On the variation with flux and frequency of the core loss coefficients in electrical machines

A model of core losses, in which the hysteresis coefficients are variable with the frequency and induction (flux density) and the eddy-current and excess loss coefficients are variable only with the induction, is proposed. A procedure for identifying the model coefficients from multifrequency Epstein tests is described, and examples are provided for three typical grades of non-grain-oriented laminated steel suitable for electric motor manufacturing. Over a wide range of frequencies between 20-400 Hz and inductions from 0.05 to 2 T, the new model yielded much lower errors for the specific core losses than conventional models. The applicability of the model for electric machine analysis is also discussed, and examples from an interior permanent-magnet and an induction motor are included.
Plasmonic-metal nanostructures for efficient conversion of solar to chemical energy.

Recent years have seen a renewed interest in the harvesting and conversion of solar energy. Among various technologies, the direct conversion of solar to chemical energy using photocatalysts has received significant attention. Although heterogeneous photocatalysts are almost exclusively semiconductors, it has been demonstrated recently that plasmonic nanostructures of noble metals (mainly silver and gold) also show significant promise. Here we review recent progress in using plasmonic metallic nanostructures in the field of photocatalysis. We focus on plasmon-enhanced water splitting on composite photocatalysts containing semiconductor and plasmonic-metal building blocks, and recently reported plasmon-mediated photocatalytic reactions on plasmonic nanostructures of noble metals. We also discuss the areas where major advancements are needed to move the field of plasmon-mediated photocatalysis forward.
Analysis of rotor eddy current losses in slotless high-speed permanent magnet machines

Rotor eddy current losses are one of the main reasons of permanent magnet demagnetization in high-speed permanent magnet machines. In this paper the rotor eddy current losses of high-speed permanent magnet machines with different slotless windings have been analysed. The analysis of the losses was performed using 2D and 3D analytical models. In the study, test machines with different windings and the same torque production capability have been analysed. Presented paper shows the dependency of rotor eddy current losses on sine- and square-wave PWM supply voltages and rotor sleeve properties. Several recommendations for reduction of rotor eddy current losses in high-speed permanent magnet machines are given.
Bilingual Lexicon Induction from Non-Parallel Data with Minimal Supervision

Building bilingual lexica from non-parallel data is a longstanding natural language processing research problem that could benefit thousands of resource-scarce languages which lack parallel data. Recent advances of continuous word representations have opened up new possibilities for this task, e.g. by establishing cross-lingual mapping between word embeddings via a seed lexicon. The method is however unreliable when there are only a limited number of seeds, which is a reasonable setting for resource-scarce languages. We tackle the limitation by introducing a novel matching mechanism into bilingual word representation learning. It captures extra translation pairs exposed by the seeds to incrementally improve the bilingual word embeddings. In our experiments, we find the matching mechanism to substantially improve the quality of the bilingual vector space, which in turn allows us to induce better bilingual lexica with seeds as few as 10.
Probabilistic Alert Correlation

With the growing deployment of host and network intrusion detection systems, managing reports from these systems becomes critically important. We present a probabilistic approach to alert correlation, extending ideas from multisensor data fusion. Features used for alert correlation are based on alert content that anticipates evolving IETF standards. The probabilistic approach provides a unified mathematical framework for correlating alerts that match closely but not perfectly, where the minimum degree of match required to fuse alerts is controlled by a single configurable parameter. Only features in common are considered in the fusion algorithm. For each feature we define an appropriate similarity function. The overall similarity is weighted by a specifiable expectation of similarity. In addition, a minimum similarity may be specified for some or all features. Features in this set must match at least as well as the minimum similarity specification in order to combine alerts, regardless of the goodness of match on the feature set as a whole. Our approach correlates attacks over time, correlates reports from heterogeneous sensors, and correlates multiple attack steps.
Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems

Although e-learning is in its infancy in Saudi Arabia, most of the public universities in the country show a great interest in the adoption of learning and teaching tools. Determining the significance of a particular tool and predicting the success of implantation is essential prior to its adoption. This paper presents and modifies the technology acceptance model (TAM) in an attempt to assist public universities, particularly in Saudi Arabia, in predicting the behavioural intention to use learning management systems (LMS). This study proposed a theoretical framework that includes the core constructs in TAM: namely, perceived ease of use, perceived usefulness, and attitude toward usage. Additional external variables were also adopted— namely, the lack of LMS availability, prior experience (LMS usage experience), and job relevance. The overall research model suggests that all mentioned variables either directly or indirectly affect the overall behavioural intention to use an LMS. Initial findings suggest the applicability of using TAM to measure the behavioural intention to use an LMS. Further, the results confirm the original TAM’s findings. Technology Acceptance Model; Higher education; Learning management systems; Saudi Arabia
A Framework for Co-simulation of AI Tools with Power Systems Analysis Software

As the smart grid initiative is pushing for smarter controls, the need for artificial intelligence-based decision-making tools, such as agent-based programs, is becoming more prevalent. However, these tools alone are not sufficient to study the behavior of the algorithms in simulations. They need to be interfaced with power systems analysis tools such as PowerWorld Simulator. This paper proposes a framework for co-simulation with two types of tools: a) MathWorks Matlab and PowerWorld, and b) the multiagent middleware JADE and PowerWorld. The source code for this framework has been made available in the public domain for the research community.
Neural Networks: Tricks of the Trade

The convergence of back-propagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many undesirable behaviors of backprop can be avoided with tricks that are rarely exposed in serious technical publications. This paper gives some of those tricks, and offers explanations of why they work. Many authors have suggested that second-order optimization methods are advantageous for neural net training. It is shown that most “classical” second-order methods are impractical for large neural networks. A few methods are proposed that do not have these limitations.
An elegant sufficiency: load-aware differentiated scheduling of data transfers

We investigate the file transfer scheduling problem, where transfers among different endpoints must be scheduled to maximize pertinent metrics. We propose two new algorithms that exploit the fact that the aggregate bandwidth obtained over a network or at a storage system tends to increase with the number of concurrent transfers---but only up to a certain limit. The first algorithm, SEAL, uses runtime information and data-driven models to approximate system load and adapt transfer schedules and concurrency so as to maximize performance while avoiding saturation. We implement this algorithm using GridFTP as the transfer protocol and evaluate it using real transfer logs in a production WAN environment. Results show that SEAL can improve average slowdowns and turnaround times by up to 25% and worst-case slowdown and turnaround times by up to 50%, compared with the best-performing baseline scheme. Our second algorithm, STEAL, further leverages user-supplied categorization of transfers as either "interactive" (requiring immediate processing) or "batch" (less time-critical). Results show that STEAL reduces the average slowdown of interactive transfers by 63% compared to the best-performing baseline and by 21% compared to SEAL. For batch transfers, compared to the best-performing baseline, STEAL improves by 18% the utilization of the bandwidth unused by interactive transfers. By elegantly ensuring a sufficient, but not excessive, allocation of concurrency to the right transfers, we significantly improve overall performance despite constraints.
Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems

The use of wearable sensors for home monitoring provides an effective means of inferring a patient’s level of activity. However, wearable sensors have intrinsic ambiguities that prevent certain activities to be recognized accurately. The purpose of this paper is to introduce a robust framework for enhanced activity recognition by integrating an ear-worn activity recognition (e-AR) sensor with ambient blob-based vision sensors. Accelerometer information from the e-AR is fused with features extracted from the vision sensor by using a Gaussian Mixture Model Bayes classifier. The experimental results showed a significant improvement of the classification accuracy compared to the use of the e-AR sensor alone.  blob sensor, wearable sensor, sensor fusion, activity recognition
A Unified Multilingual Semantic Representation of Concepts

Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN, which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance on several standard datasets.
Comparison of different soft grippers for lunch box packaging

Automating the lunch box packaging is a challenging task due to the high deformability and large individual differences in shape and physical property of food materials. Soft robotic grippers showed potentials to perform such tasks. In this paper, we presented four pneumatic soft actuators made of different materials and different fabrication methods and compared their performances through a series of tests. We found that the actuators fabricated by 3D printing showed better linearity and less individual differences, but showed low durability compared to actuators fabricated by traditional casting process. Robotic grippers were assembled using the soft actuators, and grasping tests were performed on soft paper containers filled with food materials. Results suggested that grippers with softer actuators required lower air pressure to lift up the same weight and generated less deformation on the soft container. The actuator made of casting process with Dragon Skin 10 material lifted the most weight among different actuators.