nils-herrmann/clean_scirepeval huggingface dataset:
DVB-S2 modem algorithms design and performance over typical satellite channels

In this paper we propose a design of the main modulation and demodulation units of a modem compliant with the new DVB-S2 standard ( 2004; 22:249–268). A typical satellite channel model consistent with the targeted applications of the aforementioned standard is assumed. In particular, non-linear pre-compensation as well as synchronization techniques are described in detail and their performance assessed by means of analysis and computer simulations. The proposed algorithms are shown to provide a good trade-off between complexity and performance and they apply to both the broadcast and the unicast profiles, the latter allowing the exploitation of adaptive coding and modulation (ACM) (Proceedings of the 20th AIAA Satellite Communication Systems Conference, Montreal, AIAA-paper 2002-1863, May 2002). Finally, end-to-end system performances in term of BER versus the signal-to-noise ratio are shown as a result of extensive computer simulations. The whole communication chain is modelled in these simulations, including the BCH and LDPC coder, the modulator with the pre-distortion techniques, the satellite transponder model with its typical impairments, the downlink chain inclusive of the RF-front-end phase noise, the demodulator with the synchronization sub-system units and finally the LDPC and BCH decoders.
Artificial General Intelligence

Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify – for example by changing its own source code. As we continue to create more and more intelligent agents, chances increase that they will learn about this ability. The question is: will they want to use it? For example, highly intelligent systems may find ways to change their goals to something more easily achievable, thereby ‘escaping’ the control of their creators. In an important paper, Omohundro (2008) argued that goal preservation is a fundamental drive of any intelligent system, since a goal is more likely to be achieved if future versions of the agent strive towards the same goal. In this paper, we formalise this argument in general reinforcement learning, and explore situations where it fails. Our conclusion is that the self-modification possibility is harmless if and only if the value function of the agent anticipates the consequences of self-modifications and use the current utility function when evaluating the future.
Comparison of switching strategies for an ultrasonic cleaner

This paper presents the comparison of Control Signal Patterns in ultrasonic cleaner by using the microcontroller for generating Pulse-Width Modulation (PWM), Phase-Shift PWM control (PS-PWM) and Pulse Density Modulation (PDM) patterns. These control patterns are fed in the full-bridge inverter to drive the ultrasonic transducers. The resonant frequency of 30 kHz is tested in order to find the efficiency of ultrasonic cleaner which one should be the best. Ultrasonic transducer can generate ultrasonic wave to transfer electrical energy to mechanical energy. Cavitation and implosion are occurred in liquid when ultrasonic wave travels through liquid medium in the process of cleaning.
On the Sensor Design of Torque Controlled Actuators: A Comparison Study of Strain Gauge and Encoder-Based Principles

Joint torque sensing represents one of the foundations and vital components of modern robotic systems that target to match closely the physical interaction performance of biological systems through the realization of torque controlled actuators. However, despite decades of studies on the development of different torque sensors, the design of accurate and reliable torque sensors still remains challenging for the majority of the robotics community preventing the use of the technology. This letter proposes and evaluates two joint torque sensing elements based on strain gauge and deflection-encoder principles. The two designs are elaborated and their performance from different perspectives and practical factors are evaluated including resolution, nonaxial moments load crosstalk, torque ripple rejection, bandwidth, noise/residual offset level, and thermal/time dependent signal drift. The letter reveals the practical details and the pros and cons of each sensor principle providing valuable contributions into the field toward the realization of higher fidelity joint torque sensing performance.
A Simple Method for Commonsense Reasoning

Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset . In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.
Model-Based Dynamic Cost Estimation and Tracking Method for Agile Software Development

In this paper, we present a software cost estimation model for agile development which can help estimate concrete development costs for the desired features of a product and track the project progress dynamically. In general, existing cost estimation methods for agile developments used a story point. Because it is relative value, the estimation results tend to be easily fluctuated by the small variation of the baseline story point. For tracking project’s progress, the velocity was measured to check the progress and was used to make plan for the iteration and the releases of the project. The proposed method in this paper provides the systematic estimation and dynamic tracking methodology for agile projects. To estimate the effort of a project development, function points are used in addition to the story point. The function points are determined based on the user stories of desired features of the product. We adopt the Kalman filter algorithm for tracking project progress. The remaining function points at a certain point during the project are modeled as the state space model for the Kalman filter. The daily variation of the function point is observed and inputted to the Kalman Filter for providing concrete estimation and velocity. Moreover we validate the better performance of our model by comparing with traditional methods through a case study.
Vehicle Detection and Tracking for Traffic Monitoring System

This paper presents a real-time video traffic monitoring application based on object detection and tracking, for determining traffic parameters such as vehicle velocity and number of vehicles. In detection step, background modeling approach based on edge information is proposed for separating moving foreground objects from the background. An advantage of edge is more robust to lighting changes in outdoor environments and requires significantly less computing resource. In tracking step, optical flow Lucas-Kanade (Pyramid) is applied to track each segmented object. The proposed system was evaluated on six video sequences recorded in various daytime environment
Deep Learning for RFID-Based Activity Recognition

We present a system for activity recognition from passive RFID data using a deep convolutional neural network. We directly feed the RFID data into a deep convolutional neural network for activity recognition instead of selecting features and using a cascade structure that first detects object use from RFID data followed by predicting the activity. Because our system treats activity recognition as a multi-class classification problem, it is scalable for applications with large number of activity classes. We tested our system using RFID data collected in a trauma room, including 14 hours of RFID data from 16 actual trauma resuscitations. Our system outperformed existing systems developed for activity recognition and achieved similar performance with process-phase detection as systems that require wearable sensors or manually-generated input. We also analyzed the strengths and limitations of our current deep learning architecture for activity recognition from RFID data.
Twitter: A Good Place to Detect Health Conditions

With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.
Interactive augmented reality techniques for construction at a distance of 3D geometry

This paper presents a series of new augmented reality user interaction techniques to support the capture and creation of 3D geometry of large outdoor structures, part of an overall concept we have named construction at a distance. We use information about the user's physical presence, along with hand and head gestures, to allow the user to capture and create the geometry of objects that are orders of magnitude larger than themselves, with no prior information or assistance. Using augmented reality and these new techniques, users can enter geometry and verify its accuracy in real time. This paper includes a number of examples showing objects that have been modelled in the physical world, demonstrating the usefulness of the techniques.
Facial Expression Recognition with CNN Ensemble

This paper is focusing on the Facial Expression Recognition (FER) problem from a single face image. Inspired by the advances Convolutional Neural Networks (CNNs) have achieved in image recognition and classification, we propose a CNN-based approach to address this problem. Our model consists of several different structured subnets. Each subnet is a compact CNN model trained separately. The whole network is structured by assembling these subnets together. We trained and evaluated our model on the FER2013 dataset[7]. The best single subnet achieved 62.44% accuracy and the whole model scored 65.03% accuracy, which is ranked 9th and 5th respectively among all other participants.
Management of resource constrained devices in the internet of things

The embedded computing devices deployed within the Internet of Things are expected to be resource constrained. This resource constraint not only applies to memory and processing capabilities, but the low-power radio standards utilized further constrain the network interfaces. The IPv6 protocol provides a suitable basis for interoperability in the IoT, due to its large address space and a number of existing protocols that function over IP and its flexibility. We investigate how existing IP-based network management protocols can be implemented on resource-constrained devices. We present the resource requirements for SNMP and NETCONF on an 8-bit AVR based device.
Seeing, acting, understanding: motor resonance in language comprehension.

Observing actions and understanding sentences about actions activates corresponding motor processes in the observer-comprehender. In 5 experiments, the authors addressed 2 novel questions regarding language-based motor resonance. The 1st question asks whether visual motion that is associated with an action produces motor resonance in sentence comprehension. The 2nd question asks whether motor resonance is modulated during sentence comprehension. The authors' experiments provide an affirmative response to both questions. A rotating visual stimulus affects both actual manual rotation and the comprehension of manual rotation sentences. Motor resonance is modulated by the linguistic input and is a rather immediate and localized phenomenon. The results are discussed in the context of theories of action observation and mental simulation.
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping

The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. However, such a model could still make mistakes if its features favor a wrong entity type. In this paper, we utilize Wikipedia as an open knowledge base to improve multilingual NER systems. Central to our approach is the construction of high-accuracy, highcoverage multilingual Wikipedia entity type mappings. These mappings are built from weakly annotated data and can be extended to new languages with no human annotation or language-dependent knowledge involved. Based on these mappings, we develop several approaches to improve an NER system. We evaluate the performance of the approaches via experiments on NER systems trained for 6 languages. Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18.3 F1 score improvement).
Blockchain-based payment collection supervision system using pervasive Bitcoin digital wallet

After years of tremendous development and research in digital currency, the most famous Bitcoin industry chain has been gradually completed including mining, exchange, currency exchange, ATM, pervasive digital wallet design and so on. Especially, its blockchain technology has become FinTech organizations' emerging business and research directions, also been applied in the interdisciplinary medical science, supply chain and Internet of things. Digitizing currency can solve many problems in physical currency, such as the rampant counterfeit banknotes. Thus, not only the transparency can be cost-effectively preserved in store's ledger, but also customer's rights and interests can be protected while using the digital wallet. For the government, the regulation and auditing of financial transaction can be made simpler and more convenient for tax collection issue. Digitized transaction details can be much easier to audit by any computing device than manually audit the conventional ledger books. Furthermore, to make merchandise store to use digitized currency much easier, in this paper, we propose and deploy a Bitcoin collection supervision system called BPCSS based on blockchain technology with cloud databases for customers and merchandise stores. The preliminary experimental results via the well-known pervasive digital wallet of Testnet Bitcoin demonstrate the proposed BPCSS can cost-effectively collect payment and supervise the transactions between customer and merchandise store running the implemented NFC-enabled Android Apps.
Detection of review spam: A survey

In recent years, online reviews have become the most important resource of customers’ opinions. These reviews are used increasingly by individuals and organizations to make purchase and business decisions. Unfortunately, driven by the desire for profit or publicity, fraudsters have produced deceptive (spam) reviews. The fraudsters’ activities mislead potential customers and organizations reshaping their businesses and prevent opinion-mining techniques from reaching accurate conclusions. The present research focuses on systematically analyzing and categorizingmodels that detect review spam. Next, the study proceeds to assess them in terms of accuracy and results. We find that studies can be categorized into three groups that focus on methods to detect spam reviews, individual spammers and group spam. Different detection techniques have different strengths and weaknesses and thus favor different detection contexts.
Evaluating LoRa energy efficiency for adaptive networks: From star to mesh topologies

The LoRa technology has emerged as an interesting solution for low power, long range loT applications by proposing multiple “degrees of freedom” at the physical layer. This flexibility provides either a long range at the cost of a lower data rate or higher throughput at the cost of low sensitivity, so a shorter range. In this paper, we analyze the flexibility of LoRa and propose various strategies to adapt its radio parameters (such as the spreading factor, bandwidth, and transmission power) to different deployment scenarios. We compute the energy consumption of LoRa transceivers using various radio configurations in both star and mesh topologies. Our simulation results show that in a star topology, we can achieve the optimal scaling-up/down strategy of LoRa radio parameters to obtain either a high data rate or a long range while respecting low energy consumption. In mesh networks, energy consumption is optimized by exploiting various radio configurations and the network topology (e.g., the number of hops, the network density, the cell coverage). Finally, we propose a strategy to take advantage of both star and mesh topologies.
Spectrum-efficient and scalable elastic optical path network: architecture, benefits, and enabling technologies

The sustained growth of data traffic volume calls for an introduction of an efficient and scalable transport platform for links of 100 Gb/s and beyond in the future optical network. In this article, after briefly reviewing the existing major technology options, we propose a novel, spectrum- efficient, and scalable optical transport network architecture called SLICE. The SLICE architecture enables sub-wavelength, superwavelength, and multiple-rate data traffic accommodation in a highly spectrum-efficient manner, thereby providing a fractional bandwidth service. Dynamic bandwidth variation of elastic optical paths provides network operators with new business opportunities offering cost-effective and highly available connectivity services through time-dependent bandwidth sharing, energy-efficient network operation, and highly survivable restoration with bandwidth squeezing. We also discuss an optical orthogonal frequency-division multiplexing-based flexible-rate transponder and a bandwidth-variable wavelength cross-connect as the enabling technologies of SLICE concept. Finally, we present the performance evaluation and technical challenges that arise in this new network architecture.
Correlation Matrix Memories

A new model for associative memory, based on a correlation matrix, is suggested. In this model information is accumulated on memory elements as products of component data. Denoting a key vector by q(p), and the data associated with it by another vector x(p), the pairs (q(p), x(p)) are memorized in the form of a matrix {see the Equation in PDF File} where c is a constant. A randomly selected subset of the elements of Mxq can also be used for memorizing. The recalling of a particular datum x(r) is made by a transformation x(r)=Mxqq(r). This model is failure tolerant and facilitates associative search of information; these are properties that are usually assigned to holographic memories. Two classes of memories are discussed: a complete correlation matrix memory (CCMM), and randomly organized incomplete correlation matrix memories (ICMM). The data recalled from the latter are stochastic variables but the fidelity of recall is shown to have a deterministic limit if the number of memory elements grows without limits. A special case of correlation matrix memories is the auto-associative memory in which any part of the memorized information can be used as a key. The memories are selective with respect to accumulated data. The ICMM exhibits adaptive improvement under certain circumstances. It is also suggested that correlation matrix memories could be applied for the classification of data.
Effective information technology (IT) governance mechanisms: An IT outsourcing perspective

Effective IT governance will ensure alignment between IT and business goals. Organizations with ineffective IT governance will suffer due to poor performance of IT resources such as inaccurate information quality, inefficient operating costs, runaway IT project and even the demise of its IT department. This study seeks to examine empirically the individual IT governance mechanisms that influence the overall effectiveness of IT governance. Furthermore, this study examines the relationship of effective IT governance, the extent of IT outsourcing decisions within the organizations, and the level of IT Intensity in the organizations. We used structural equation modeling analysis to examine 110 responses from members of ISACA (Information Systems and Audit Control Association) Australia in which their organizations have outsourced their IT functions. Results suggest significant positive relationships between the overall level of effective IT governance and the following mechanisms: the involvement of senior management in IT, the existence of ethic or culture of compliance in IT, and corporate communication systems.
A Novel Switched-Current Successive Approximation ADC

A novel switched-current successive approximation ADC is presented in this paper with high speed and low power consumption. The proposed ADC contains a new high-accuracy and power-e±cient switched-current S/H circuit and a speed-improved current comparator. Designed and simulated in a 0:18m CMOS process, this 8-bit ADC achieves 46.23 dB SNDR at 1.23 MS/s consuming 73:19 W under 1.2 V voltage supply, resulting in an ENOB of 7.38-bit and an FOM of 0.357 pJ/Conv.-step.
Energy Efficient Resource Management in Virtualized Cloud Data Centers

Rapid growth of the demand for computational power by scientific, business and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. We propose an energy efficient resource management system for virtualized Cloud data centers that reduces operational costs and provides required Quality of Service (QoS). Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs and thermal state of computing nodes. We present first results of simulation-driven evaluation of heuristics for dynamic reallocation of VMs using live migration according to current requirements for CPU performance. The results show that the proposed technique brings substantial energy savings, while ensuring reliable QoS. This justifies further investigation and development of the proposed resource management system.