nils-herrmann/clean_scirepeval huggingface dataset:
Adapting the Lesk Algorithm for Word Sense Disambiguation to WordNet by Satanjeev Banerjee

All human languages have words that can mean different things in different contexts. Word sense disambiguation is the process of automatically figuring out the intended meaning of such a word when used in a sentence. One of the several approaches proposed in the past is Michael Lesk’s 1986 algorithm. This algorithm is based on two assumptions. First, when two words are used in close proximity in a sentence, they must be talking of a related topic and second, if one sense each of the two words can be used to talk of the same topic, then their dictionary definitions must use some common words. For example, when the words ”pine cone” occur together, they are talking of ”evergreen trees”, and indeed one meaning each of these two words has the words ”evergreen” and ”tree” in their definitions. Thus we can disambiguate neighbouring words in a sentence by comparing their definitions and picking those senses whose definitions have the most number of common words. The biggest drawback of this algorithm is that dictionary definitions are often very short and just do not have enough words for this algorithm to work well. We deal with this problem by adapting this algorithm to the semantically organized lexical database called WordNet. Besides storing words and their meaning like a normal dictionary, WordNet also ”connects” related words together. We overcome the problem of short definitions by looking for common words not only between the definitions of the words being disambiguated, but also between the definitions of words that are closely related to them in WordNet. We show that our algorithm achieves an 83% improvement in accuracy over the standard Lesk algorithm, and that it compares favourably with other systems evaluated on the same data.
Design of high PSRR folded cascode operational amplifier for LDO applications

This paper presents a novel CMOS folded cascode operational amplifier that leads to high PSRR and provides gain nearly equal to that of a two stage op-amp. The proposed design is implemented in GPDK 0.18μm CMOS technology. This op-amp uses a folded cascode structure in the output stage combined with the differential amplifier having PMOS input transistors to achieve good input common mode range and lower flicker noise. It has an important feature that it allows the input common mode level close to supply voltage. The proposed topology improves the PSRR of op-amp which can be used for LDO applications. Simulations using Cadence under 1.8 V show a DC gain of 72.0404 dB and a phase margin of 62.4636 degree at a unity gain bandwidth of 13.33 MHz with the power consumption smaller than 0.13 mW along with a PSRR of 72.0966 dB. The layout of the design shows that the area acquired on the chip is approximately equal to 8897.27 μm2.
Coordination mechanisms of supply chain systems

Supply chain management (SCM) has become an important management paradigm. As supply chain members are often separate and independent economic entities, a key issue in SCM is to develop mechanisms that can align their objectives and coordinate their activities so as to optimize system performance. In this paper, we provide a review of coordination mechanisms of supply chain systems in a framework that is based on supply chain decision structure and nature of demand. This framework highlights the behavioral aspects and information need in the coordination of a supply chain. The identification of these issues points out several directions of future research in this area.
Effects of Field of View on Presence, Enjoyment, Memory, and Simulator Sickness in a Virtual Environment

Effects of field-of-view (FOV) in a virtual environment (VE) on presence, enjoyment, memory, and simulator sickness (SS) were studied. A refined scale designed to assess subjects’ engagement, enjoyment, and immersion (EI) was developed. Items to examine subjects’ memory of the VE were included. SS was examined using the Simulator Sickness Questionnaire (SSQ). Using a withinsubjects design, data were collected from 10 subjects at four FOVs (60o, 100o, 140o, and 180o). The VE, Crayolaland, was presented in a driving simulator. Results indicated that presence, enjoyment, and SS varied as a function of display FOV. Subjects exhibited higher SSQ and presence subscale scores with increasing FOV. SSQ and presence values approached asymptotes for FOVs beyond 140°. Presence and SS were positively correlated; enjoyment and SS were negatively correlated.
Methodological aspects of EEG and body dynamics measurements during motion

EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.
Skin Reading: encoding text in a 6-channel haptic display

This paper investigates the communication of natural language messages using a wearable haptic display. Our research spans both the design of the haptic display, as well as the methods for communication that use it. First, three wearable configurations are proposed basing on haptic perception fundamentals. To encode symbols, we devise an overlapping spatiotemporal stimulation (OST) method, that distributes stimuli spatially and temporally with a minima gap. An empirical study shows that, compared with spatial stimulation, OST is preferred in terms of recall. Second, we propose an encoding for the entire English alphabet and a training method for letters, words and phrases. A second study investigates communication accuracy. It puts four participants through five sessions, for an overall training time of approximately 5 hours per participant. Results reveal that after one hour of training, participants were able to discern 16 letters, and identify two- and three-letter words. They could discern the full English alphabet (26 letters, 92% accuracy) after approximately three hours of training, and after five hours participants were able to interpret words transmitted at an average duration of 0.6s per word.
Item recommending by item co-purchasing network and user preference

A novel recommendation approach based on multiple-objectives optimization is introduced. It utilizes histories of item co-purchases as represented by directed graphs and histories of user preferences for items as represented by user-item rating matrices to create models of user behavior. Herein, multiple objectives for item recommendations are considering to maximize the ratings and rankings of each item as derived from user preferences and co-purchasing networks of items, respectively. Finally, optimal items, called Pareto optimal solutions, will be recommended.
A hybrid method consisting of GA and SVM for intrusion detection system

In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is proposed and its implementation in intrusion detection problem is explained. The proposed hybrid algorithm is employed in reducing the number of features from 45 to 10. The features are categorized into three priorities using GA algorithm as the highest important is the first priority and the lowest important is placed in the third priority. The feature distribution is done in a way that 4 features are placed in the first priority, 4 features in the second, and 2 features in the third priority. The results reveal that the proposed hybrid algorithm is capable of achieving a true-positive value of 0.973, while the false-positive value is 0.017.
A New High-Temperature Superconducting Vernier Permanent-Magnet Machine for Wind Turbines

A vernier permanent magnet (VPM) machine using high-temperature superconducting (HTS) winding for dual excitation is proposed for wind power generation. The proposed VPM machine adopts the parallel hybrid excitation configuration, the stator consists of an identical tooth pair connected together by the stator yoke and the rotor contains two sets of consequent-pole permanent magnets joined together by the rotor yoke. The armature winding is located at the stator main slots while the field winding using HTS coils is wound on the stator yoke. With the benefit of the HTS field winding, the air-gap flux can be flexibly adjusted by controlling the magnitude of the dc current. Therefore, the machine performance is improved to cope with different operating conditions.
Sociable driving agents to maintain driver's attention in autonomous driving

Recently, many studies have been conducted on increasing the automation level in cars to achieve safer and more efficient transportation. The increased automation level creates room for the drivers to shift their attention to non-driving related activities. However, there are cases that cannot be handled by automation where a driver should take over the control. This pilot study investigates a paradigm for keeping the drivers' situation-awareness active during autonomous driving by utilizing a social robot system, NAMIDA. NAMIDA is an interface consisting of three sociable driving agents that can interact with the driver through eye-gaze behaviors. We analyzed the effectiveness of NAMIDA on maintaining the drivers' attention to the road, by evaluating the response time of the drivers to a critical situation on the road. An experiment consisting of a take over scenario was conducted in a dynamic driving simulator. The results showed that existence of NAMIDA significantly reduced the response time of the drivers. However, surprisingly, NAMIDA without eye-gaze behaviors was more effective in reducing the response time than NAMIDA with eye-gaze behaviors. Additionally, the results revealed better subjective impressions for NAMIDA with eye-gaze behaviors behaviors.
Smooth assembled mappings for large-scale real walking

Virtual reality applications prefer real walking to provide highly immersive presence than other locomotive methods. Mapping-based techniques are very effective for supporting real walking in small physical workspaces while exploring large virtual scenes. However, the existing methods for computing real walking maps suffer from poor quality due to distortion. In this paper, we present a novel divide-and-conquer method, called Smooth Assembly Mapping (SAM), to compute real walking maps with low isometric distortion for large-scale virtual scenes. First, the input virtual scene is decomposed into a set of smaller local patches. Then, a group of local patches is mapped together into a real workspace by minimizing a low isometric distortion energy with smoothness constraints between the adjacent patches. All local patches are mapped and assembled one by one to obtain a complete map. Finally, a global optimization is adopted to further reduce the distortion throughout the entire map. Our method easily handles teleportation technique by computing maps of individual regions and assembling them with teleporter conformity constraints. A large number of experiments, including formative user studies and comparisons, have shown that our method succeeds in generating high-quality real walking maps from large-scale virtual scenes to small real workspaces and is demonstrably superior to state-of-the-art methods.
Low-Cost Binary128 Floating-Point FMA Unit Design with SIMD Support

Binary64 arithmetic is rapidly becoming inadequate to cope with today's large-scale computations due to an accumulation of errors. Therefore, binary128 arithmetic is now required to increase the accuracy and reliability of these computations. At the same time, an obvious trend emerging in modern processors is to extend their instruction sets by allowing single instruction multiple data (SIMD) execution, which can significantly accelerate the data-parallel applications. To address the combined demands mentioned above, this paper presents the architecture of a low-cost binary128 floating-point fused multiply add (FMA) unit with SIMD support. The proposed FMA design can execute a binary128 FMA every other cycle with a latency of four cycles, or two binary64 FMAs fully pipelined with a latency of three cycles, or four binary32 FMAs fully pipelined with a latency of three cycles. We use two binary64 FMA units to support binary128 FMA which requires much less hardware than a fully pipelined binary128 FMA. The presented binary128 FMA design uses both segmentation and iteration hardware vectorization methods to trade off performance, such as throughput and latency, against area and power. Compared with a standard binary128 FMA implementation, the proposed FMA design has 30 percent less area and 29 percent less dynamic power dissipation.
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.
Design a single band microstrip patch antenna at 60 GHz millimeter wave for 5G application

This proposed paper, a single band microstrip patch antenna for 5G wireless application is presented. This proposed antenna is suitable for the millimeter wave frequency. The single band antenna consist of new H slot and E slot loaded on the radiating patch with the 50 ohms microstrip line feeding used. This single band antenna is simulated on a Rogers RT5880 dielectric substrate have relative permittivity 2.2, loss tangent 0.0009, and height 1.6mm. The antenna is simulated by Electromagnetic simulation, computer software technology Microwave studio. The proposed single band antenna and simulated result on return loss, VSWR, surface current and 3D radiation pattern is presented. The simulated antenna shows the return loss −40.99dB at 60 GHz millimeter wave 5G wireless application presented.
A CMOS low-power low-offset and high-speed fully dynamic latched comparator

This paper presents a novel dynamic latched comparator that demonstrates lower offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage is improved. The complementary version of the regenerative latch stage, which provides larger output drive current than the conventional one at a limited area, is implemented. The proposed circuit is designed using 90nm CMOS technology and 1V power supply voltage, and it demonstrates up to 19% less offset voltage and 62% less sensitivity of the delay to the input voltage difference (17ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption.
A Language Support for Cloud Elasticity Management

Elasticity is the intrinsic element that differentiates Cloud computing from traditional computing paradigm, since it allows service providers to rapidly adjust their needs for resources to absorb the demand and hence guarantee a minimum level of Quality of Service (QoS) that respects the Service Level Agreements (SLAs) previously defined with their clients. However, due to non-negligible resource initiation time, network fluctuations or unpredictable workload, it becomes hard to guarantee QoS levels and SLA violations may occur. This paper proposes a language support for Cloud elasticity management that relies on CSLA (Cloud Service Level Agreement). CSLA offers new features such as QoS/functionality degradation and an advanced penalty model that allow providers to finely express contracts so that services self-adaptation capabilities are improved and SLA violations minimized. The approach was evaluated with a real infrastructure and application test bed. Experimental results show that the use of CSLA makes Cloud services capable of absorbing more peaks and oscillations by trading-off the QoS levels and costs due to penalties.
FACON: A flow-aided cooperative navigation scheme

Navigation for autonomous underwater vehicles (AUVs) in mid-depth water column is notoriously challenging due to the lack of features for reference. In this paper, we propose a flow-aided cooperative navigation (FACON) strategy to improve the navigation performance of a team of AUVs when neither frequent surfacing nor persistent bottom-locking is available. Preloaded ocean current forecasts are referenced by each individual vehicle as it performs dead-reckoning with an inertial navigation system and measures relative current velocities during navigation. A marginalized particle filter is applied by each AUV to track its location, velocity, sensor biases, and local flow perturbation unresolved by the ocean forecast. Meanwhile, AUVs perform distributed cooperative localization based on relative pose measurements and asynchronized local communication when they enter the communication range of one another. Opportunistic information fusion among AUVs is realized through covariance intersection. The performance of the flow-aided navigation scheme of a single AUV is analyzed within a simulated experiment based on field test data. The feasibility of FACON is discussed through simulation in a turbulent, multi-gyre flow field.
End to End Automation on Cloud with Build Pipeline: The Case for DevOps in Insurance Industry, Continuous Integration, Continuous Testing, and Continuous Delivery

In modern environment, delivering innovative idea in a fast and reliable manner is extremely significant for any organizations. In the existing scenario, Insurance industry need to better respond to dynamic market requirements, faster time to market for new initiatives and services, and support innovative ways of customer interaction. In past few years, the transition to cloud platforms has given benefits such as agility, scalability, and lower capital costs but the application lifecycle management practices are slow with this disruptive change. DevOps culture extends the agile methodology to rapidly create applications and deliver them across environment in automated manner to improve performance and quality assurance. Continuous Integration (CI) and Continuous delivery (CD) has emerged as a boon for traditional application development and release management practices to provide the capability to release quality artifacts continuously to customers with continuously integrated feedback. The objective of the paper is to create a proof of concept for designing an effective framework for continuous integration, continuous testing, and continuous delivery to automate the source code compilation, code analysis, test execution, packaging, infrastructure provisioning, deployment, and notifications using build pipeline concept.
Diagnostic criteria for malingered neurocognitive dysfunction: proposed standards for clinical practice and research.

Over the past 10 years, widespread and concerted research efforts have led to increasingly sophisticated and efficient methods and instruments for detecting exaggeration or fabrication of cognitive dysfunction. Despite these psychometric advances, the process of diagnosing malingering remains difficult and largely idiosyncratic. This article presents a proposed set of diagnostic criteria that define psychometric, behavioral, and collateral data indicative of possible, probable, and definite malingering of cognitive dysfunction, for use in clinical practice and for defining populations for clinical research. Relevant literature is reviewed, and limitations and benefits of the proposed criteria are discussed.
An under-actuated mechanism for a robotic finger

Robotic hands can be applied in different applications such as prosthesis, humanoid robots, industrial robotic manipulators and other kinds of robotic arms. Introduction of robotic technology into the field of prosthesis has resulted a higher quality of life for amputees. In this paper an under-actuated mechanism which has the self-adaptation ability is proposed to be used in the fingers of the hand prosthesis. The mechanism is a modification of the cross bar mechanism and it shows grasping adaptation ability for different geometries. In addition the mechanism is capable to generate dexterous grasping patterns making a paradigm shift from the conventional linkage mechanisms used for fingers which only has the capability for the grasping. Kinematic analysis, mathematical simulation and computer simulations were carried out to evaluate the effectiveness of the mechanism. Furthermore a new parameter, degree of adaptation is introduced to evaluate the performance of the under-actuated finger mechanisms.
Understanding Individual Neuron Importance Using Information Theory

In this work, we characterize the outputs of individual neurons in a trained feed-forward neural network by entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence. By cumulatively ablating neurons in the network, we connect these information-theoretic measures to the impact their removal has on classification performance on the test set. We observe that, looking at the neural network as a whole, none of these measures is a good indicator for classification performance, thus confirming recent results by Morcos et al. However, looking at specific layers separately, both mutual information and class selectivity are positively correlated with classification performance. We thus conclude that it is ill-advised to compare these measures across layers, and that different layers may be most appropriately characterized by different measures. We then discuss pruning neurons from neural networks to reduce computational complexity of inference. Drawing from our results, we perform pruning based on information-theoretic measures on a fully connected feed-forward neural network with two hidden layers trained on MNIST dataset and compare the results to a recently proposed pruning method. We furthermore show that the common practice of re-training after pruning can partly be obviated by a surgery step called bias balancing, without incurring significant performance degradation.