nils-herrmann/clean_scirepeval huggingface dataset:
Neural-Symbolic Intuitionistic Reasoning

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. Already in the pioneering days of computational models of neural cognition, the question was raised how symbolic knowledge can be represented and dealt with within neural networks. The landmark paper [McCulloch and Pitts, 1943] provides fundamental insights how propositional logic can be processed using simple artificial neural networks. Within the following decades, however, the topic did not receive much attention as research in artificial intelligence initially focused on purely symbolic approaches. The power of machine learning using artificial neural networking was not recognized until the 80s, when in particular the backpropagation algorithm [Rumelhart et al., 1986] made connectionist learning feasible and applicable in practice. These advances indicated a breakthrough in machine learning which quickly led to industrial-strength applications in areas such as image analysis, speech and pattern recognition, investment analysis, engine monitoring, fault diagnosis, etc. During a training process from raw data, artificial neural networks acquire expert knowledge about the problem domain, and the ability to generalize this knowledge to similar but previously unencountered situations in a way which often surpasses the abilities of human experts. The knowledge obtained during the training process, however, is hidden within
Design of a self-oscillating class D power amplifier for piezoelectric actuators

A design of a delay based self-oscillating class-D power amplifier for piezoelectric actuators is presented and modelled. First order and second order configurations are discussed in detail and analytical results reveal the stability criteria of a second order system, which should be respected in the design. It also shows if the second order system converges, it will tend to give a correct pulse modulation regarding to the input modulation index. Experimental results show the effectiveness of this design procedure. For a piezoelectric load of 400 nF, powered by a 150 V 10 kHz sinusoidal signal, a total harmonic distortion (THD) of 4.3% is obtained.
Airborne network evaluation: challenges and high fidelity emulation solution

The future airborne network (AN) is envisioned as an IP-based hierarchical network with heterogeneous nodes and waveforms. Due to its highly dynamic nature and bandwidth constraints, new AN technologies are needed to provide reliable network operations with similar levels of mission support as that in terrestrial networks. This paper identifies challenges in designing and evaluating AN technologies and also describes an on-going effort on developing a realistic wireless testing and performance evaluation framework for AN testing and evaluation. A set of use cases are presented to illustrate how the framework can be used to evaluate new architecture and protocols in AN environments.
Common Subspace Learning via Cross-Domain Extreme Learning Machine

Extreme learning machine (ELM) is proposed for solving a single-layer feed-forward network (SLFN) with fast learning speed and has been confirmed to be effective and efficient for pattern classification and regression in different fields. ELM originally focuses on the supervised, semi-supervised, and unsupervised learning problems, but just in the single domain. To our best knowledge, ELM with cross-domain learning capability in subspace learning has not been exploited very well. Inspired by a cognitive-based extreme learning machine technique (Cognit Comput. 6:376–390, 1; Cognit Comput. 7:263–278, 2.), this paper proposes a unified subspace transfer framework called cross-domain extreme learning machine (CdELM), which aims at learning a common (shared) subspace across domains. Three merits of the proposed CdELM are included: (1) A cross-domain subspace shared by source and target domains is achieved based on domain adaptation; (2) ELM is well exploited in the cross-domain shared subspace learning framework, and a new perspective is brought for ELM theory in heterogeneous data analysis; (3) the proposed method is a subspace learning framework and can be combined with different classifiers in recognition phase, such as ELM, SVM, nearest neighbor, etc. Experiments on our electronic nose olfaction datasets demonstrate that the proposed CdELM method significantly outperforms other compared methods.
Bio-impedance characterization technique with implantable neural stimulator using biphasic current stimulus

Knowledge of the bio-impedance and its equivalent circuit model at the electrode-electrolyte/tissue interface is important in the application of functional electrical stimulation. Impedance can be used as a merit to evaluate the proximity between electrodes and targeted tissues. Understanding the equivalent circuit parameters of the electrode can further be leveraged to set a safe boundary for stimulus parameters in order not to exceed the water window of electrodes. In this paper, we present an impedance characterization technique and implement a proof-of-concept system using an implantable neural stimulator and an off-the-shelf microcontroller. The proposed technique yields the parameters of the equivalent circuit of an electrode through large signal analysis by injecting a single low-intensity biphasic current stimulus with deliberately inserted inter-pulse delay and by acquiring the transient electrode voltage at three well-specified timings. Using low-intensity stimulus allows the derivation of electrode double layer capacitance since capacitive charge-injection dominates when electrode overpotential is small. Insertion of the inter-pulse delay creates a controlled discharge time to estimate the Faradic resistance. The proposed method has been validated by measuring the impedance of a) an emulated Randles cells made of discrete circuit components and b) a custom-made platinum electrode array in-vitro, and comparing estimated parameters with the results derived from an impedance analyzer. The proposed technique can be integrated into implantable or commercial neural stimulator system at low extra power consumption, low extra-hardware cost, and light computation.
Furrows and dermal ridges of the hand in patients with alcohol embryopathy

Palmar creases and dermal ridge patterns of 34 patients with alcohol embryopathy are compared with 470 healthy individuals. In alcohol embryopathy several typical deviations were noted.  The interdigital part of the distal palmar crease is generally sharply bent, the proximal transverse crease is hypoplastic or missing, the thenar crease is commonly well marked. Simian creases and bridged palmar creases are more common in patients with alcohol embryopathy than in healthy individuals. Ridge Patterns of the Palm. The main line D coming from triradius d in patients with alcohol embryopathy mostly shows a low type of ending in the fourth interdigital area; in this area loops are twice as common as in healthy individuals. Patterns of the Fingertips. No deviations were noted in the distribution of whorls and loops, but virtually no arches were observed in patients with alcohol embryopathy. These anomalies suggest embryonic damage in the twelvth week of gestation.
Periodontal Health Re-Establishment in Cleft Lip and Palate Patients through Vestibuloplasty Associated with Free Gingival Graft

The primary objectives of periodontal therapy are to maintain and to obtain health and integrity of the insertion apparatus and to re-establish esthetics by means of the quantitative and qualitative restoration of the gingival margin. Esthetics can be considered essential to the success of any dental procedure. However, in cleft lip and palate patients gingival esthetics do not play a relevant role, since most patients present little gingiva exposure (Mikami, 1990). The treatment protocol for cleft palate patients is complex and often requires a myriad of surgical and rehabilitative procedures that last until adulthood. In order to rehabilitate these patients and provide them with adequate physical and psychological conditions for a good quality of life, plastic surgery has been taking place since the 19th century, with the development of new techniques. By the age of six months the patients have undergone lip repair procedures (Bill, 1956; Jolleys, 1954), followed by palatoplasty at the age of 1218 months. As a consequence of these surgical interventions, the formation of innumerous scars and fibrous tissue in the anterior region may cause some sequels, such as orofacial growth alterations (Quarta and Koch, 1989; Ozawa, 2001), a shallow vestibule with lack of attached gingiva and gingival margin mobility (Falcone, 1966). A shallow vestibule in the cleft lip and palate patient is associated with the contraction of the upper lip during healing (Iino et al, 2001), which causes deleterious effects on growth, facial expression, speech, orthodontic and prosthetic treatment problems, diminished keratinized gingiva, bone graft resorption and changes in the upper lip muscle pattern. The surgical protocol at the Hospital for Rehabilitation of Craniofacial Anomalies (HRCA) in Bauru consists of carrying out primary surgeries (cheiloplasty and palatoplasty) during the first months of Periodontal Health Re-Establishment in Cleft Lip and Palate Patients through Vestibuloplasty Associated with Free Gingival Graft
Corner Block List: An Effective and Efficient Topological Representation of Non-Slicing Floorplan

In this paper, a corner block list -- a new efficient topological representation for non-slicing floorplan is proposed with applications to VLSI floorplan and building block placement. Given a corner block list, it takes only linear time to construct the floorplan. Unlike the O-tree structure, which determines the exact floorplan based on given block sizes, corner block list defines the floorplan independent of the block sizes. Thus, the structure is better suited for floorplan optimization with various size configurations of each block. Based on this new structure and the simulated annealing technique, an efficient floorplan algorithm is given. Soft blocks and the aspect ratio of the chip are taken into account in the simulated annealing process. The experimental results demonstrate the algorithm is quite promising.
DEVELOPMENT OF A CURRENT CONTROL ULTRACAPACITOR CHARGER BASED ON DIGITAL SIGNAL PROCESSING

Ultracapacitor usually use as a short-term duration electrical energy storage because it has several advantages, like high power density (5kW/kg), long lifecycle and very good charge/discharge efficiency. Unlike batteries, ultracapacitors may be charged and discharged at similar rates. This is very useful in energy recovery systems such as dynamic braking of transport systems. Here are a few characteristics of ultracapacitors that should be kept in mind when integrating/designing a charging system for the intended application. An ultracapacitor with zero charge looks like a short circuit to the charging source. Most of low cost power supplies fold back the output current in response to a perceived short circuit, making them unsuitable for charging of ultracapacitors. Ultracapacitors have a low series inductance allowing easy stabilizing with switch mode chargers. The RC time constant of passive charging networks is usually too long. Therefore, linear regulators are inefficient components for ultracapacitor charging. In this paper, the development of a current control ultracapacitor charger based on Digital Signal Processing (DSP) is presented. Keyword: current control, DSP, ultracapacitor, ultracapacitor charger
Additive Outliers ( AO ) and Innovative Outliers ( IO ) in GARCH ( 1 , 1 ) Processes

This study is about outlier detection in time series data. The main objective is to derive and to test statistics for detecting additive outlier (AO) and innovative outlier (IO) in GARCH(1,1) processes and subsequently to develop a procedure for testing the presence of outliers using the statistics. A test statistic has been derived for each type of outlier. In the derivation of the statistics, the method applied was to derive outlier detection statistics for GARCH(1,1) by taking the analogy of GARCH(1,1) as being equivalent to ARMA(1,1) for the 2 t ε ( t ε being the residual series). Because of the difficulty in determining the exact sampling distributions of the outlier detecting statistics, critical regions were estimated through simulations. The performance of the outlier detection was evaluated based on the outlier test criteria and the outlier detection procedure, using simulations. Results on the power of correctly detecting the outlier using the outlier test criteria and the power of correctly identifying the type of outlier, given that the location is correctly detected were reported. This was done for each type of outlier, individually. In this study the developed outlier detection procedure was applied for testing the presence of the two outlier types in the daily observations of the Kuala Lumpur Composite Index (KLCI). An outlier was found to be present in year 1998 which corresponded to the economic downturn of the 1997-1998 periods.
A Survey on Software-Defined Networking

Emerging mega-trends (e.g., mobile, social, cloud, and big data) in information and communication technologies (ICT) are commanding new challenges to future Internet, for which ubiquitous accessibility, high bandwidth, and dynamic management are crucial. However, traditional approaches based on manual configuration of proprietary devices are cumbersome and error-prone, and they cannot fully utilize the capability of physical network infrastructure. Recently, software-defined networking (SDN) has been touted as one of the most promising solutions for future Internet. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. As a result, SDN is positioned to provide more efficient configuration, better performance, and higher flexibility to accommodate innovative network designs. This paper surveys latest developments in this active research area of SDN. We first present a generally accepted definition for SDN with the aforementioned two characteristic features and potential benefits of SDN. We then dwell on its three-layer architecture, including an infrastructure layer, a control layer, and an application layer, and substantiate each layer with existing research efforts and its related research areas. We follow that with an overview of the de facto SDN implementation (i.e., OpenFlow). Finally, we conclude this survey paper with some suggested open research challenges.
Odd type DCT/DST for video coding: Relationships and low-complexity implementations

In this paper, we show a class of relationships which link Discrete Cosine Transforms (DCT) and Discrete Sine Transforms (DST) of types V, VI, VII and VIII, which have been recently considered for inclusion in the future video coding technology. In particular, the proposed relationships allow to compute the DCT-V and the DCT-VIII as functions of the DCT-VI and the DST-VII respectively, plus simple reordering and sign-inversion. Moreover, this paper exploits the proposed relationships and the Winograd factorization of the Discrete Fourier Transform to construct low-complexity factorizations for computing the DCT-V and the DCT-VIII of length 4 and 8. Finally, the proposed signal-flow-graphs have been implemented using an FPGA technology, thus showing reduced hardware utilization with respect to the direct implementation of the matrix-vector multiplication algorithm.
FFT-based deep learning deployment in embedded systems

Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the embedded platforms with intensive computation and storage. Researchers have investigated on reducing DNN model size with negligible accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN training and inference model suitable for embedded platforms with reduced asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. We develop the training and inference algorithms based on FFT as the computing kernel and deploy the FFT-based inference model on embedded platforms achieving extraordinary processing speed.
A 0.27V 30MHz 17.7nJ/transform 1024-pt complex FFT core with super-pipelining

Recently, aggressive voltage scaling was shown as an important technique in achieving highly energy-efficient circuits. Specifically, scaling Vdd to near or sub-threshold regions was proposed for energy-constrained sensor systems to enable long lifetime and small system volume [1][2][4]. However, energy efficiency degrades below a certain voltage, Vmin, due to rapidly increasing leakage energy consumption, setting a fundamental limit on the achievable energy efficiency. In addition, voltage scaling degrades performance and heightens delay variability due to large Id sensitivity to PVT variations in the ultra-low voltage (ULV) regime. This paper uses circuit and architectural methods to further reduce the minimum energy point, or Emin, and establish a new lower limit on energy efficiency, while simultaneously improving performance and robustness. The approaches are demonstrated on an FFT core in 65nm CMOS.
EVALUATION OF BUCKET CAPACITY , DIGGING FORCE CALCULATIONS AND STATIC FORCE ANALYSIS OF MINI HYDRAULIC BACKHOE EXCAVATOR

Rapidly growing rate of industry of earth moving machines is assured through the high performance construction machineries with complex mechanism and automation of construction activity. Design of backhoe link mechanism is critical task in context of digging force developed through actuators during the digging operation. The digging forces developed by actuators must be greater than that of the resistive forces offered by the terrain to be excavated. This paper focuses on the evaluation method of bucket capacity and digging forces required to dig the terrain for light duty construction work. This method provides the prediction of digging forces and can be applied for autonomous operation of excavation task. The evaluated digging forces can be used as boundary condition and loading conditions to carry out Finite Element Analysis of the backhoe mechanism for strength and stress analysis. A generalized breakout force and digging force model also developed using the fundamentals of kinematics of backhoe mechanism in context of robotics. An analytical approach provided for static force analysis of mini hydraulic backhoe excavator attachment.
WiGest: A ubiquitous WiFi-based gesture recognition system

We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modifications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy increases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.
A 20/20 Vision of the VLDB-2020?

A 20/20 vision in ophthalmology implies a perfect view of things that are in front of you. The term is also used to mean a perfect sight of the things to come. Here we focus on a speculative vision of the VLDB in the year 2020. This panel is the follow-up of the one I organised (with S. Navathe) at the Kyoto VLDB in 1986, with the title: "Anyone for a VLDB in the Year 2000?". In that panel, the members discussed the major advances made in the database area and conjectured on its future, following a concern of many researchers that the database area was running out of interesting research topics and therefore it might disappear into other research topics, such as software engineering, operating systems and distributed systems. That did not happen.
Solving real-world cutting stock-problems in the paper industry: Mathematical approaches, experience and challenges

We discuss cutting stock problems (CSPs) from the perspective of the paper industry and the financial impact they make. Exact solution approaches and heuristics have been used for decades to support cutting stock decisions in that industry. We have developed polylithic solution techniques integrated in our ERP system to solve a variety of cutting stock problems occurring in real world problems. Among them is the simultaneous minimization of the number of rolls and the number of patterns while not allowing any overproduction. For two cases, CSPs minimizing underproduction and CSPs with master rolls of different widths and availability, we have developed new column generation approaches. The methods are numerically tested using real world data instances. An assembly of current solved and unsolved standard and non-standard CSPs at the forefront of research are put in perspective.