nils-herrmann/clean_scirepeval huggingface dataset:
NOMA: An Information Theoretic Perspective

In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated from an informationtheoretic perspective. The relationships among the capacity r egion of broadcast channels and two rate regions achieved by NOMA and time-division multiple access (TDMA) are illustrated first. Then, the performance of NOMA is evaluated by considering TDMA as the benchmark, where both the sum rate and the individual use r rates are used as the criteria. In a wireless downlink scenar io with user pairing, the developed analytical results show that NOMA can outperform TDMA not only for the sum rate but also for each user’s individual rate, particularly when the difference between the users’ channels is large. I. I NTRODUCTION Because of its superior spectral efficiency, non-orthogona l multiple access (NOMA) has been recognized as a promising technique to be used in the fifth generation (5G) networks  – . NOMA utilizes the power domain for achieving multiple access, i.e., different users are served at different power levels. Unlike conventional orthogonal MA, such as timedivision multiple access (TDMA), NOMA faces strong cochannel interference between different users, and success ive interference cancellation (SIC) is used by the NOMA users with better channel conditions for interference managemen t. The concept of NOMA is essentially a special case of superposition coding developed for broadcast channels (BC ). Cover first found the capacity region of a degraded discrete memoryless BC by using superposition coding . Then, the capacity region of the Gaussian BC with single-antenna terminals was established in . Moreover, the capacity re gion of the multiple-input multiple-output (MIMO) Gaussian BC was found in , by applying dirty paper coding (DPC) instea d of superposition coding. This paper mainly focuses on the single-antenna scenario. Specifically, consider a Gaussian BC with a single-antenna transmitter and two single-antenna receivers, where each r eceiver is corrupted by additive Gaussian noise with unit var iance. Denote the ordered channel gains from the transmitter to the two receivers byhw andhb, i.e., |hw| < |hb|. For a given channel pair(hw, hb), the capacity region is given by  C , ⋃ a1+a2=1, a1, a2 ≥ 0 { (R1, R2) : R1, R2 ≥ 0, R1≤ log2 ( 1+ a1x 1+a2x ) , R2≤ log2 (1+a2y) }
An Effective News Recommendation Method for Microblog User

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data.
Compact SIW leaky wave antenna

A compact Substrate Integrated Waveguide (SIW) Leaky-Wave Antenna (LWA) is proposed. Internal vias are inserted in the SIW in order to have narrow walls, and so reducing the size of the SIW-LWA, the new structure is called Slow Wave - Substrate Integrated Waveguide - Leaky Wave Antenna (SW-SIW-LWA), since inserting the vias induce the SW effect. After designing the antenna and simulating with HFSS a reduction of 30% of the transverse side of the antenna is attained while maintaining an acceptable gain. Other parameters like the radiation efficiency, Gain, directivity, and radiation pattern are analyzed. Finally a Comparison of our miniaturization technique with Half-Mode Substrate Integrated Waveguide (HMSIW) technique realized in recent articles is done, shows that SW-SIW-LWA technique could be a good candidate for SIW miniaturization.
Review of microgrid technology

The use of renewable energy source (RES) in meet the demand of electrical energy is getting into attention as solution of the problem a deficit of electrical energy. Application of RES in electricity generation system is done in a variety of configurations, among others in microgrid system. Implementation of microgrid systems provide many advantages both from the user and from the electric utility provider. Many microgrid development carried out in several countries, because microgrid offers many advantages, including better power quality and more environmentally friendly. Microgrid development concern in technology generation, microgrid architecture, power electronics, control systems, protection systems. This paper reviewing various technological developments related to microgrid system and case study about microgrid system development using grid tie inverter (GTI). Microgrid system can implemented using GTI, power transfer can occur from GTI to grid when GTI has power excess and grid supplying power to GTI when GTI power shortage.
Diffusion tensor imaging of the corpus callosum in Autism

The corpus callosum is the largest commissural white matter pathway that connects the hemispheres of the human brain. In this study, diffusion tensor imaging (DTI) was performed on subject groups with high-functioning autism and controls matched for age, handedness, IQ, and head size. DTI and volumetric measurements of the total corpus callosum and subregions (genu, body and splenium) were made and compared between groups. The results showed that there were significant differences in volume, fractional anisotropy, mean diffusivity, and radial diffusivity between groups. These group differences appeared to be driven by a subgroup of the autism group that had small corpus callosum volumes, high mean diffusivity, low anisotropy, and increased radial diffusivity. This subgroup had significantly lower performance IQ measures than either the other individuals with autism or the control subjects. Measurements of radial diffusivity also appeared to be correlated with processing speed measured during the performance IQ tests. The subgroup of autism subjects with high mean diffusivity and low fractional anisotropy appeared to cluster with the highest radial diffusivities and slowest processing speeds. These results suggest that the microstructure of the corpus callosum is affected in autism, which may be related to nonverbal cognitive performance.
Learning to Write Notes in Electronic Health Records

Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient’s medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete.
A compact size and high efficiency CMOS-IPD rectenna using 2.5D wafer-level packing for a wireless power harvesting system

This work achieves a two-and-a-half-dimensional (2.5D) wafer-level radio frequency (RF) energy harvesting rectenna module with a compact size and high power conversion efficiency (PCE) that integrates a 2.45 GHz antenna in an integrated passive device (IPD) and a rectifier in a tsmcTM 0.18 μm CMOS process. The proposed rectifier provides a master-slave voltage doubling full-wave topology which can reach relatively high PCE by means of a relatively simple circuitry. The IPD antenna was stacked on top of the CMOS rectifier. The rectenna (including an antenna and rectifier) achieves an output voltage of 1.2 V and PCE of 47 % when the operation frequency is 2.45 GHz, with −12 dBm input power. The peak efficiency of the circuit is 83 % with −4 dBm input power. The die size of the RF harvesting module is less than 1 cm2. The performance of this module makes it possible to energy mobile device and it is also very suitable for wearable and implantable wireless sensor networks (WSN).
Iot based school bus tracking and arrival time prediction

Nowadays, parents are perturbed about school going children because of the increasing number of cases of missing students. On occasion, students need to wait a much longer time for arrival of their school bus. There exist some communication technologies that are used to ensure the safety of students. But these are incapable of providing efficient services to parents. This paper presents the development of a school bus monitoring system, capable of providing productive services through emerging technologies like Internet of Things (Iota). The proposed IoT based system tracks students in a school bus using a combination of RFID/GPS/GSM/GPRS technologies. In addition to the tracking, a prediction algorithm is implemented for computation of the arrival time of a school-bus. Through an Android application, parents can continuously monitor the bus route and forecast arrival time of the bus.
Probiotics in the intestinal tract of juvenile whiteleg shrimp Litopenaeus vannamei: modulation of the bacterial community.

Molecular analysis of the 16S rDNA of the intestinal microbiota of whiteleg shrimp Litopenaeus vannamei was examined to investigate the effect of a Bacillus mix (Bacillus endophyticus YC3-b, Bacillus endophyticus C2-2, Bacillus tequilensisYC5-2) and the commercial probiotic (Alibio(®)) on intestinal bacterial communities and resistance to Vibrio infection. PCR and single strain conformation polymorphism (SSCP) analyses were then performed on DNA extracted directly from guts. Injection of shrimp with V. parahaemolyticus at 2.5 × 10(5) CFU g(-1) per shrimp followed 168 h after inoculation with Bacillus mix or the Alibio probiotic or the positive control. Diversity analyses showed that the bacterial community resulting from the Bacillus mix had the highest diversity and evenness and the bacterial community of the control had the lowest diversity. The bacterial community treated with probiotics mainly consisted of α- and γ-proteobacteria, fusobacteria, sphingobacteria, and flavobacteria, while the control mainly consisted of α-proteobacteria and flavobacteria. Differences were grouped using principal component analyses of PCR-SSCP of the microbiota, according to the time of inoculation. In Vibrio parahaemolyticus-infected shrimp, the Bacillus mix (~33 %) induced a significant increase in survival compared to Alibio (~21 %) and the control (~9 %). We conclude that administration of the Bacillus mix induced modulation of the intestinal microbiota of L. vannamei and increased its resistance to V. parahaemolyticus.
Description and analysis of a bottom-up DFA minimization algorithm

We establish linear-time reductions between the minimization of a deterministic finite automaton (DFA) and the conjunction of 3 subproblems: the minimization of a strongly connected DFA, the isomorphism problem for a set of strongly connected minimized DFAs, and the minimization of a connected DFA consisting in two strongly connected components, both of which are minimized. We apply this procedure to minimize, in linear time, automata whose nontrivial strongly connected com-
Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator

Current neural networks are accumulating accolades for their performance on a variety of real-world computational tasks including recognition, classification, regression, and prediction, yet there are few scalable architectures that have emerged to address the challenges posed by their computation. This paper introduces Minitaur, an event-driven neural network accelerator, which is designed for low power and high performance. As an field-programmable gate array-based system, it can be integrated into existing robotics or it can offload computationally expensive neural network tasks from the CPU. The version presented here implements a spiking deep network which achieves 19 million postsynaptic currents per second on 1.5 W of power and supports up to 65 K neurons per board. The system records 92% accuracy on the MNIST handwritten digit classification and 71% accuracy on the 20 newsgroups classification data set. Due to its event-driven nature, it allows for trading off between accuracy and latency.
Universal arrhenius temperature activated charge transport in diodes from disordered organic semiconductors.

Charge transport models developed for disordered organic semiconductors predict a non-Arrhenius temperature dependence ln(mu) proportional, variant1/T(2) for the mobility mu. We demonstrate that in space-charge limited diodes the hole mobility (micro(h)) of a large variety of organic semiconductors shows a universal Arrhenius temperature dependence micro(h)(T) = micro(0)exp(-Delta/kT) at low fields, due to the presence of extrinsic carriers from the Ohmic contact. The transport in a range of organic semiconductors, with a variation in room temperature mobility of more than 6 orders of magnitude, is characterized by a universal mobility micro(0) of 30-40 cm(2)/V s. As a result, we can predict the full temperature dependence of their charge transport properties with only the mobility at one temperature known.
Multiple models of Bayesian networks applied to offline recognition of Arabic handwritten city names

In this paper we address the problem of offline Arabic handwriting word recognition. Offline recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwritten words recognition using multiple Bayesian networks. First, we cut the image in several blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including Zernik and Hu moments. Finally, we apply four variants of Bayesian networks classifiers (Naïve Bayes, Tree Augmented Naïve Bayes (TAN), Forest Augmented Naïve Bayes (FAN) and DBN (dynamic bayesian network) to classify the whole image of tunisian city name. The results demonstrate FAN and DBN outperform good recognition rates.
The Three Cycle View of Design Science

As a commentary to Juhani Iivari’s insightful essay, I briefly analyze design science research as an embodiment of three closely related cycles of activities. The Relevance Cycle inputs requirements from the contextual environment into the research and introduces the research artifacts into environmental field testing. The Rigor Cycle provides grounding theories and methods along with domain experience and expertise from the foundations knowledge base into the research and adds the new knowledge generated by the research to the growing knowledge base. The central Design Cycle supports a tighter loop of research activity for the construction and evaluation of design artifacts and processes. The recognition of these three cycles in a research project clearly positions and differentiates design science from other research paradigms. The commentary concludes with a claim to the pragmatic nature of design science.
Compressed sensing of ECG bio-signals using one-bit measurement matrices

Compressed sensing (CS) is an emerging signal processing technique that enables sub-Nyquist sampling of sparse signals such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) bio-signals. Future CS signal processing systems will exploit significant time- and/or frequency-domain sparsity to achieve ultra-low-power bio-signal acquisition in the analog, digital, or mixed-signal domains. A measurement matrix of random values is key to one form of CS computation. It has been shown for ECG and EMG signals that signal-to-quantization noise ratios (SQNR) > 60 dB with compression factors up to 16X are achievable using uniform or Gaussian 6-bit random coefficients. In this paper, 1-bit random coefficients are shown also to give compression factors up to 16X with similar SQNR performance. This approach reduces hardware and saves energy concomitant with 1-bit versus 6-bit signal processing.
Energy Harvesting Using a Low-Cost Rectenna for Internet of Things (IoT) Applications

Traditionally employed human-to-human and human-to-machine communication has recently been replaced by a new trend known as the Internet of things (IoT). IoT enables device-to-device communication without any human intervention, hence, offers many challenges. In this paradigm, machine’s self-sustainability due to limited energy capabilities presents a great challenge. Therefore, this paper proposed a low-cost energy harvesting device using rectenna to mitigate the problem in the areas where battery constraint issues arise. So, an energy harvester is designed, optimized, fabricated, and characterized for energy harvesting and IoT applications which simply recycles radio-frequency (RF) energy at 2.4 GHz, from nearby Wi-Fi/WLAN devices and converts them to useful dc power. The physical model comprises of antenna, filters, rectifier, and so on. A rectangular patch antenna is designed and optimized to resonate at 2.4 GHz using the well-known transmission-line model while the band-pass and low-pass filters are designed using lumped components. Schottky diode (HSMS-2820) is used for rectification. The circuit is designed and fabricated using the low-cost FR4 substrate (<inline-formula> <tex-math notation="LaTeX">${h}$ </tex-math></inline-formula> = 16 mm and <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{r} = 4.6$ </tex-math></inline-formula>) having the fabricated dimensions of 285 mm <inline-formula> <tex-math notation="LaTeX">$\times \,\,90$ </tex-math></inline-formula> mm. Universal software radio peripheral and GNU Radio are employed to measure the received RF power, while similar measurements are carried out using R&S spectrum analyzer for validation. The received measured power is −64.4 dBm at the output port of the rectenna circuit. Hence, our design enables a pervasive deployment of self-operable next-generation IoT devices.
Multi-view low-rank dictionary learning for image classification

Recently, multi-view dictionary learning (DL) technique has received much attention. Although some multi-view DL methods have been presented, they suffer from the problem of performance degeneration when large noise exists in multiple views. In this paper, we propose a novel multi-view DL approach named multi-view low-rank DL (MLDL) for image classification. Specifically, inspired by the low-rank matrix recovery theory, we provide a multi-view dictionary low-rank regularization term to solve the noise problem. We further design a structural incoherence constraint for multi-view DL, such that redundancy among dictionaries of different views can be reduced. In addition, to enhance efficiency of the classification procedure, we design a classification scheme for MLDL, which is based on the idea of collaborative representation based classification. We apply MLDL for face recognition, object classification and digit classification tasks. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.
Privacy, identity and security in ambient intelligence: A scenario analysis

The success of Ambient Intelligence (AmI) will depend on how secure it can be made, how privacy and other rights of individuals can be protected and how individuals can come to trust the intelligent world that surrounds them and through which they move. This article addresses these issues by analysing scenarios for ambient intelligence applications that have been developed over the last few years. It elaborates the assumptions that promotors make about the likely use of the technology and possibly unwanted side effects. It concludes with a number of threats for personal privacy that become evident.
Digital Watermarking Using MATLAB

Embedding a hidden stream of bits in a file is called Digital Watermarking. The file could be an image, audio, video or text. Nowadays, digital watermarking has many applications such as broadcast monitoring, owner identification, proof of ownership, transaction tracking, content authentication, copy control, device control, and file reconstruction (Cox et. al., 2008). In literature, the host file is called the “asset”, and the bit stream is called the “message”. The main specifications of a watermarking system are: Robustness (Against intentional attacks or unintentional ones such as compression), Imperceptibility, and Capacity. Importance of each depends on the application. As a matter of fact there is a trade-off between these factors (Barni & Bartolini, 2004). Although watermarking in some literature includes visible imprints, here we only mean the invisible embedding of the data. In this chapter, we will introduce how to use MATLAB to implement image watermarking algorithms. These algorithms include the most famous ones which are widely used in current literature or more complicated approaches are based upon. These are commonly divided into three categories (Barni & Bartolini, 2004) 1. Watermarking in Spatial Domain 2. Watermarking in Spectral Domain 3. Watermarking in Hybrid Domain In section 2 we will go through some basic image processing commands in MATLAB. Section 3 provides information about different fundamental watermarking methods. Evaluating the algorithms is discussed in Section 4, and finally section 5 brings a conclusion.