nils-herrmann/clean_scirepeval huggingface dataset:
A Review of Feature Extraction in Sentiment Analysis

Rapid increase in internet users along with growing power of online review sites and social media has given birth to Sentiment analysis or Opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. Therefore product features or aspects have got significant role in sentiment analysis. In addition to sufficient work being performed in text analytics, feature extraction in sentiment analysis is now becoming an active area of research. This review paper discusses existing techniques and approaches for feature extraction in sentiment analysis and opinion mining. In this review we have adopted a systematic literature review process to identify areas well focused by researchers, least addressed areas are also highlighted giving an opportunity to researchers for further work. We have also tried to identify most and least commonly used feature selection techniques to find research gaps for future work. Rapid increase in internet users along with growing power of online review sites and social media has given birth to Sentiment analysis or Opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. Therefore product features or aspects have got significant role in sentiment analysis. In addition to sufficient work being performed in text analytics, feature extraction in sentiment analysis is now becoming an active area of research. This review paper discusses existing techniques and approaches for feature extraction in sentiment analysis and opinion mining. In this review we have adopted a systematic literature review process to identify areas well focused by researchers, least addressed areas are also highlighted giving an opportunity to researchers for further work. We have also tried to identify most and least commonly used feature selection techniques to find research gaps for future work.
Ink-Jet Printed Nanoparticle Microelectromechanical Systems

We report a method to additively build threedimensional (3-D) microelectromechanical systems (MEMS) and electrical circuitry by ink-jet printing nanoparticle metal colloids. Fabricating metallic structures from nanoparticles avoids the extreme processing conditions required for standard lithographic fabrication and molten-metal-droplet deposition. Nanoparticles typically measure 1 to 100 nm in diameter and can be sintered at plastic-compatible temperatures as low as 300 C to form material nearly indistinguishable from the bulk material. Multiple ink-jet print heads mounted to a computer-controlled 3-axis gantry deposit the 10% by weight metal colloid ink layer-by-layer onto a heated substrate to make two-dimensional (2-D) and 3-D structures. We report a high-Q resonant inductive coil, linear and rotary electrostatic-drive motors, and in-plane and vertical electrothermal actuators. The devices, printed in minutes with a 100 m feature size, were made out of silver and gold material with high conductivity,and feature as many as 400 layers, insulators, 10 : 1 vertical aspect ratios, and etch-released mechanical structure. These results suggest a route to a desktop or large-area MEMS fabrication system characterized by many layers, low cost, and data-driven fabrication for rapid turn-around time, and represent the first use of ink-jet printing to build active MEMS.
A Novel Photometric Method for Real-Time 3D Reconstruction of Fingerprint

3D fingerprint recognition is an emerging technology in biometrics. However, current 3D fingerprint acquisition systems are usually with complex structure and high-cost and that has become the main obstacle for its popularization. In this work, we present a novel photometric method and an experimental setup for real-time 3D fingerprint reconstruction. The proposed system consists of seven LED lights that mounted around one camera. In the surface reflectance modeling of finger surface, a simplified Hanrahan-Krueger model is introduced. And a neural network approach is used to solve the model for accurate estimation of surface normals. A calibration method is also proposed to determine the lighting directions as well as the correction of the lighting fields. Moreover, to stand out the fingerprint ridge features and get better visual effects, a linear transformation is applied to the recovered normals field. Experiments on live fingerprint and the comparison with traditional photometric stereo algorithm are used to demonstrate its high performance.
A robust and easy to implement method for IMU calibration without external equipments

Motion sensors as inertial measurement units (IMU) are widely used in robotics, for instance in the navigation and mapping tasks. Nowadays, many low cost micro electro mechanical systems (MEMS) based IMU are available off the shelf, while smartphones and similar devices are almost always equipped with low-cost embedded IMU sensors. Nevertheless, low cost IMUs are affected by systematic error given by imprecise scaling factors and axes misalignments that decrease accuracy in the position and attitudes estimation. In this paper, we propose a robust and easy to implement method to calibrate an IMU without any external equipment. The procedure is based on a multi-position scheme, providing scale and misalignments factors for both the accelerometers and gyroscopes triads, while estimating the sensor biases. Our method only requires the sensor to be moved by hand and placed in a set of different, static positions (attitudes). We describe a robust and quick calibration protocol that exploits an effective parameterless static filter to reliably detect the static intervals in the sensor measurements, where we assume local stability of the gravity's magnitude and stable temperature. We first calibrate the accelerometers triad taking measurement samples in the static intervals. We then exploit these results to calibrate the gyroscopes, employing a robust numerical integration technique. The performances of the proposed calibration technique has been successfully evaluated via extensive simulations and real experiments with a commercial IMU provided with a calibration certificate as reference data.
Link Prediction Based on Graph Neural Networks

Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, every heuristic has a strong assumption on when two nodes are likely to link, which limits their effectiveness on networks where these assumptions fail. In this regard, a more reasonable way should be learning a suitable heuristic from a given network instead of using predefined ones. By extracting a local subgraph around each target link, we aim to learn a function mapping the subgraph patterns to link existence, thus automatically learning a “heuristic” that suits the current network. In this paper, we study this heuristic learning paradigm for link prediction. First, we develop a novel γ-decaying heuristic theory. The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs. Our results show that local subgraphs reserve rich information related to link existence. Second, based on the γ-decaying theory, we propose a new method to learn heuristics from local subgraphs using a graph neural network (GNN). Its experimental results show unprecedented performance, working consistently well on a wide range of problems.
A region based active contour method for x-ray lung segmentation using prior shape and low level features

In this work, a level set energy for segmenting the lungs from digital Posterior-Anterior (PA) chest x-ray images is presented. The primary challenge in using active contours for lung segmentation is local minima due to shading effects and presence of strong edges due to the rib cage and clavicle. We have used the availability of good contrast at the lung boundaries to extract a multi-scale set of edge/corner feature points and drive our active contour model using these features. We found these features when supplemented with a simple region based data term and a shape term based on the average lung shape, able to handle the above local minima issues. The algorithm was tested on 1130 clinical images, giving promising results.
Hybrid feedforward-feedback active noise control

This work presents an architecture for single source, single point noise cancellation that seeks adequate gain margin and high performance for both stationary and nonstationary noise sources by combining feedforward and feedback control. Gain margins and noise reduction performance of the hybrid control architecture are validated experimentally using an earcup from a circumaural hearing protector. Results show that the hybrid system provides 5 to 30 dB active performance in the frequency range 50-800 Hz for tonal noise and 18-27 dB active performance in the same frequency range for nonstationary noise, such as aircraft or helicopter cockpit noise, improving low frequency (> 100 Hz) performance by up to 15 dB over either control component acting individually.
A 100 MHz Ladder FeRAM Design With Capacitance-Coupled-Bitline (CCB) Cell

This paper proposes a new ladder FeRAM ar chitecture with capacitance-coupled-bitline (CCB) cells for high-end embedded applications. The ladder FeRAM architecture short-circuits both electrodes of each ferroelectric capacitor at every standby cycle. This overcomes the fatal disturbance problem inherent to the CCB cell, and halves read/write cycle time by sharing a plateline and its driver with 32 cells in two neighboring ladder blocks. This configuration realizes small 0.35 μm2 cell using a highly reliable ferroelectric capacitor of as large as 0.145 μm2 size, and a highly compatible process with logic-LSI. A slow plateline drive of the CCB cell due to a resistive plateline using an active area is minimized to 2.5 ns by introducing thick M3 shunt-path and distributed M3 platelines. The area penalty of the shunt is 4.7% of an array. A serious bitline-to-bitline coupling noise in edge bitlines up to the noise/signal ratio of 0.38 due to the operation peculiar to FeRAM is eliminated by introducing activated dummy bitlines and their sense amplifiers. The design of 16 cells in a ladder block is optimal for effective cell size, cell signal, and active power dissipation. A new early plateline pull-down read scheme omits "0"-data rewrite operation without read disturbance. A 64 Kb ladder FeRAM with the CCB cells and the early plateline pull-down read scheme achieves a fast random read/write of 10 ns cycle and 8 ns access at 150°C.
24 GHz LTCC I/Q Mixer Using Packaged HEMTs

An I/Q active mixer in LTCC technology using packaged HEMTs as mixing devices is described. A mixer is designed for use in the 24 GHz automotive radar application. An on-tile buffer amplifier was added to compensate for the limited power available from the system oscillator. Careful choice of the type or topology for each of the passive circuits implemented resulted in an optimal mixer layout, so a very small size for a ceramic tile of 15times15times0.8 mm3 was achieved. The measured conversion gain of the mixer for a 0 dBm LO level was -6.7 dB for I and -5.2 dB for Q. The amplitude imbalance between I and Q signals resulting from the aggressive miniaturization of the quadrature coupler could be compensated in the DSP stages of the system at no additional cost. The measured I-Q phase imbalance was around 3 degrees. The measured return losses at mixer ports and LO-RF isolations are also very good.
Data Loss Prevention Based on Data-Driven Usage Control

Inadvertent data disclosure by insiders is considered as one of the biggest threats for corporate information security. Data loss prevention systems typically try to cope with this problem by monitoring access to confidential data and preventing their leakage or improper handling. Current solutions in this area, however, often provide limited means to enforce more complex security policies that for instance specify temporal or cardinal constraints on the execution of events. This paper presents UC4Win, a data loss prevention solution for Microsoft Windows operating systems that is based on the concept of data-driven usage control to allow such a fine-grained policy-based protection. UC4Win is capable of detecting and controlling data-loss related events at the level of individual function calls. This is done with function call interposition techniques to intercept application calls to the Windows API in combination with methods to track the flows of confidential data through the system.
EQUALIZERS FOR HIGH-SPEED SERIAL LINKS

In this tutorial paper we present equalization techniques to mitigate inter-symbol interference (ISI) in high-speed communication links. Both transmit and receive equalizers are analyzed and high-speed circuits implementing them are presented. It is shown that a digital transmit equalizer is the simplest to design, while a continuous-time receive equalizer generally provides better performance. Decision feedback equalizer (DFE) is described and the loop latency problem is addressed. Finally, techniques to set the equalizer parameters adaptively are presented.
Adversarial Examples for Semantic Segmentation and Object Detection

It has been well demonstrated that adversarial examples, i.e., natural images with visually imperceptible perturbations added, cause deep networks to fail on image classification. In this paper, we extend adversarial examples to semantic segmentation and object detection which are much more difficult. Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e.g., the target is a pixel or a receptive field in segmentation, and an object proposal in detection). This inspires us to optimize a loss function over a set of targets for generating adversarial perturbations. Based on this, we propose a novel algorithm named Dense Adversary Generation (DAG), which applies to the state-of-the-art networks for segmentation and detection. We find that the adversarial perturbations can be transferred across networks with different training data, based on different architectures, and even for different recognition tasks. In particular, the transfer ability across networks with the same architecture is more significant than in other cases. Besides, we show that summing up heterogeneous perturbations often leads to better transfer performance, which provides an effective method of black-box adversarial attack.
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing

Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, describe formal guarantees for the method, and describe practical issues in implementing the algorithms. While our examples are predominantly drawn from the NLP literature, the material should be of general relevance to inference problems in machine learning. A central theme of this tutorial is that Lagrangian relaxation is naturally applied in conjunction with a broad class of combinatorial algorithms, allowing inference in models that go significantly beyond previous work on Lagrangian relaxation for inference in graphical models.
Investigating the success of ERP systems: Case studies in three Taiwanese high-tech industries

The measurement of enterprise resource planning (ERP) systems success or effectiveness is critical to our understanding of the value and efficacy of ERP investment and managerial actions. Whether traditional information systems success models can be extended to investigating ERP systems success is yet to be investigated. This paper proposes a partial extension and respecification of the DeLone and MacLean model of IS success to ERP systems. The purpose of the present research is to re-examine the updated DeLone and McLean model [W. DeLone, E. McLean, The DeLone McLean model of information system success: a ten-year update, Journal of Management Information Systems 19 (4) (2003) 3–9] of ERP systems success. The updated DeLone and McLean model was applied to collect data from the questionnaires answered by 204 users of ERP systems at three high-tech firms in Taiwan. Finally, this study suggests that system quality, service quality, and information quality are most important successful factors.
A Case-Based Reasoning Approach to Imitating RoboCup Players

We describe an effort to train a RoboCup soccer-playing agent playing in the Simulation League using casebased reasoning. The agent learns (builds a case base) by observing the behaviour of existing players and determining the spatial configuration of the objects the existing players pay attention to. The agent can then use the case base to determine what actions it should perform given similar spatial configurations. When observing a simple goal-driven, rule-based, stateless agent, the trained player appears to imitate the behaviour of the original and experimental results confirm the observed behaviour. The process requires little human intervention and can be used to train agents exhibiting diverse behaviour in an automated manner.
PCG biometric identification system based on feature level fusion using canonical correlation analysis

In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the system's performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.
RIO: A Pervasive RFID-based Touch Gesture Interface

In this paper, we design and develop RIO, a novel battery-free touch sensing user interface (UI) primitive for future IoT and smart spaces. RIO enables UIs to be constructed using off-the-shelf RFID readers and tags, and provides a unique approach to designing smart IoT spaces. With RIO, any surface can be turned into a touch-aware surface by simply attaching RFID tags to them. RIO also supports custom-designed RFID tags, and thus allows specially customized UIs to be easily deployed into a real-world environment. RIO is built using the technique of impedance tracking: when a human finger touches the surface of an RFID tag, the impedance of the antenna changes. This change manifests as a change in the phase of the RFID backscattered signal, and is used by RIO to track fine-grained touch movement over both off-the shelf and custom built tags. We study this impedance behavior in-depth and show how RIO is a reliable UI primitive that is robust even within a multi-tag environment. We leverage this primitive to build a prototype of RIO that can continuously locate a finger during a swipe movement to within 3 mm of its actual position. We also show how custom-design RFID tags can be built and used with RIO, and provide two example applications that demonstrate its real-world use.
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks

Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person’s signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.
Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept "Internet of Things" has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.