nils-herrmann/clean_scirepeval huggingface dataset:
Robot Learning from Demonstration: A Task-level Planning Approach:

In this paper, we deal with the problem of task level learning and planning for robotic applications that involve object manipulation. In dynamic environments, preprogramming robots for execution of complex tasks such as for example setting a dinner table is of little use, since the same order of subtasks may not be conceivable due to the changed state of the world in the execution phase. In our approach, we aim to learn the goal of the task, and use a task planner to reach the goal given different initial states of the world. However, for some tasks, there are underlying constraints that must be fulfilled, and knowing just the final goal is not sufficient. We propose two techniques for constraint identification. In the first case, the teacher can directly instruct the system about the underlying constraints. In the second case, the constraints are identified by the robot itself through the merging of multiple observations. The constraints are then considered in the planning phase, allowing the task to be executed without violating any of them. We evaluate our work on a real robot performing pick-and-place tasks with several objects of various size.
Radial-arrayed rotary electrification for high performance triboelectric generator.

Harvesting mechanical energy is an important route in obtaining cost-effective, clean and sustainable electric energy. Here we report a two-dimensional planar-structured triboelectric generator on the basis of contact electrification. The radial arrays of micro-sized sectors on the contact surfaces enable a high output power of 1.5 W (area power density of 19 mW cm(-2)) at an efficiency of 24%. The triboelectric generator can effectively harness various ambient motions, including light wind, tap water flow and normal body movement. Through a power management circuit, a triboelectric-generator-based power-supplying system can provide a constant direct-current source for sustainably driving and charging commercial electronics, immediately demonstrating the feasibility of the triboelectric generator as a practical power source. Given exceptional power density, extremely low cost and unique applicability resulting from distinctive mechanism and structure, the triboelectric generator can be applied not only to self-powered electronics but also possibly to power generation at a large scale.
Behavioral Experiments for Assessing the Abstract Argumentation Semantics of Reinstatement

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users.
Survey of platforms for massive IoT

Internet of things (IoT) becomes a prominent technology in our world. It is enabling the connection between the objects (the “things”) and the backend systems via the Internet. Everyday objects can become connected and smart. It has been adopted in different areas and applications such as smart cities, smart agriculture, smart healthcare, smart manufacturing, and others. Moreover, IoT platforms are currently growing up in the market. Each platform provides valuable and specific services and features. This paper presents a survey on IoT platforms, discussing their architectures and fundamentals of IoT building elements and communication protocols between them. This paper aims to help the reader choose a suitable and adequate IoT platform for own demands in the huge number and variety of platforms available. This survey provides a comprehensive view of the components and features of the state-of-the-art IoT platforms.
Medication adherence monitoring using modern technology

Medication adherence is a major problem in the healthcare sector, because failure to adhere to taking medication has major impact on the individual's health. Technologies such as sensor networks and RFID are being used more frequently by the wireless-enabled health applications, which make them dominant fields in the era of connected health. In this paper, we tackle some of the recent applications for medication adherence monitoring that rely on technologies such as wireless sensor networks, RFID, and others and study their advantages and disadvantages from a technical point of view. Nonetheless, as each of these technologies comes with limitations, we then provide our vision towards a non-invasive system that encompasses several sensors for the aim of performing medication adherence monitoring collaboratively, with low enough energy to make the system feasible.
Usefulness, localizability, humanness, and language-benefit: additional evaluation criteria for natural language dialogue systems

Human-computer dialogue systems interact with human users using natural language. We used the ALICE/AIML chatbot architecture as a platform to develop a range of chatbots covering different languages, genres, text-types, and user-groups, to illustrate qualitative aspects of natural language dialogue system evaluation. We present some of the different evaluation techniques used in natural language dialogue systems, including black box and glass box, comparative, quantitative, and qualitative evaluation. Four aspects of NLP dialogue system evaluation are often overlooked: “usefulness” in terms of a user’s qualitative needs, “localizability" to new genres and languages, "humanness" compared to humanhuman dialogues, and "language benefit" compared to alternative interfaces. We illustrated these aspects with respect to our work on machine-learnt chatbot dialogue systems; we believe these aspects are worthwhile in impressing potential new users and customers.
Implementation of virtual fitting room using image processing

There has been a great increase in interests towards online shopping. In case of purchase of products like apparels which always require a sense of knowledge on how cloths would fit upon a person. This is the major reason why less number of apparels are being shopped online. Hence, a virtual dressing room which would make people know how cloths personally fits in would be a great luxury for the online sellers which could give a wide choice for customers. For online marketers, this would be a great tool for enhancing its market.
Foldio: Digital Fabrication of Interactive and Shape-Changing Objects With Foldable Printed Electronics

Foldios are foldable interactive objects with embedded input sensing and output capabilities. Foldios combine the advantages of folding for thin, lightweight and shape-changing objects with the strengths of thin-film printed electronics for embedded sensing and output. To enable designers and end-users to create highly custom interactive foldable objects, we contribute a new design and fabrication approach. It makes it possible to design the foldable object in a standard 3D environment and to easily add interactive high-level controls, eliminating the need to manually design a fold pattern and low-level circuits for printed electronics. Second, we contribute a set of printable user interface controls for touch input and display output on folded objects. Moreover, we contribute controls for sensing and actuation of shape-changeable objects. We demonstrate the versatility of the approach with a variety of interactive objects that have been fabricated with this framework.
Content-based filtering algorithm for mobile recipe application

In general, people like to cook but they have no idea on what to cook and how to cook. Furthermore, they also wish to know certain recipes that suit to their preference. There are many cooking recipe websites but most of them do not provide suggestions for the user based on user's food preferences. Current recipe suggestions developed by some researchers are mainly based on the collective interests, demographic information, and ingredients content. With respect to the problem mentioned previously, a content based mobile recipe application is proposed and tested in this paper. The main objective of this proposed application is to suggest a user preferred recipe using content-based filtering algorithm. Content-based filtering algorithm (CBFA) will be applied to identify the recipes that have high possibility for user to like. This algorithm will be able to recommend recipes based on user interaction. The algorithm will consider a few attributes to identify the similarity between the recipe pages viewed by the user. The reason to choose this algorithm is because it does not have the cold-start problem and it solely depends on the active user to identify their preferences. An Android based mobile recipe application is built to test this algorithm. The results from the experiment conducted show that the proposed algorithm is effective for recommending preferred recipes to user.
Leaky-Wave Antennas

This paper gives a basic review and a summary of recent developments for leaky-wave antennas (LWAs). An LWA uses a guiding structure that supports wave propagation along the length of the structure, with the wave radiating or “leaking” continuously along the structure. Such antennas may be uniform, quasi-uniform, or periodic. After reviewing the basic physics and operating principles, a summary of some recent advances for these types of structures is given. Recent advances include structures that can scan to endfire, structures that can scan through broadside, structures that are conformal to surfaces, and structures that incorporate power recycling or include active elements. Some of these novel structures are inspired by recent advances in the metamaterials area.
Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.
Closing the Loop: Evaluating a Measurement Instrument for Maturity Model Design

To support the systematic improvement of business intelligence (BI) in organizations, we have designed and refined a BI maturity model (BIMM) and a respective measurement instrument (MI) in prior research. In this study, we devise an evaluation strategy, and evaluate the validity of the designed measurement artifact. Through cluster analysis of maturity assessments of 92 organizations, we identify characteristic BI maturity scenarios and representative cases for the relevant scenarios. For evaluating the designed instrument, we compare its results with insights obtained from in-depth interviews in the respective companies. A close match between our model's quantitative maturity assessments and the maturity levels from the qualitative analyses indicates that the MI correctly assesses BI maturity. The applied evaluation approach has the potential to be re-used in other design research studies where the validity of utility claims is often hard to prove.
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multiagent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.
An inductorless linear optical receiver for 20Gbaud/s (40Gb/s) PAM-4 modulation using 28nm CMOS

This paper1 presents a linear optical receiver designed using a 28nm CMOS technology suitable for 20Gbaud/s (40Gb/s) PAM-4 modulation. The optical receiver consists of a transimpedance amplifier (gain adjustable from 40dBΩ to 56dBO) followed by a variable gain amplifier (gain adjustable from 6dB to 17dB). Capacitive peaking is used to achieve a bandwidth of ~10GHz, thus avoiding the use of on-chip inductors which require large die area. A robust automatic gain control loop is used to ensure a constant differential output voltage swing of ~100mV for an input dynamic range of 20μA to 500μA (peak current). Over this same range, high linearity (total harmonic distortion less than 5%, 250MHz sinewave, 10harmonics taken into account) is obtained. The rms input referred noise current (integrated from 10MHz to 20GHz) was 2.5μArms. The linear optical receiver consumes 56mW from a 1.5V supply voltage.
Bayes Optimality in Linear Discriminant Analysis

We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.
Flikker: saving DRAM refresh-power through critical data partitioning

Energy has become a first-class design constraint in computer systems. Memory is a significant contributor to total system power. This paper introduces Flikker, an application-level technique to reduce refresh power in DRAM memories. Flikker enables developers to specify critical and non-critical data in programs and the runtime system allocates this data in separate parts of memory. The portion of memory containing critical data is refreshed at the regular refresh-rate, while the portion containing non-critical data is refreshed at substantially lower rates. This partitioning saves energy at the cost of a modest increase in data corruption in the non-critical data. Flikker thus exposes and leverages an interesting trade-off between energy consumption and hardware correctness. We show that many applications are naturally tolerant to errors in the non-critical data, and in the vast majority of cases, the errors have little or no impact on the application's final outcome. We also find that Flikker can save between 20-25% of the power consumed by the memory sub-system in a mobile device, with negligible impact on application performance. Flikker is implemented almost entirely in software, and requires only modest changes to the hardware.
D.L.D., Dynamic Lighting Design: Parametric Interactive Lighting Software in Urban Public Space

D.L.D., Dynamic Lighting Design, is a three year, E.U. funded, ongoing research project, concerning ways of designing dynamic luminous environments for pedestrians in open public spaces. This paper presents a set of ideas about how space and the surface of architecture are defined in a dynamic luminous environment, which enhance pedestrian lighting and encourage people to walk and visit public spaces. The D.L.D., parametric interactive lighting software in urban public space, will also be outlined in this paper.
Beauty eMakeup: A Deep Makeup Transfer System

In this demo, we present a Beauty eMakeup System to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the before-makeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Extensive experimental evaluations and analysis on testing images well demonstrate the effectiveness of the proposed system.
An Empirical Evaluation of Deep Learning for Network Anomaly Detection

Deep learning has been given a great deal of attention with its success story in many areas such as image analysis and speech recognition. In particular, deep learning is good at dealing with high-dimensional data exhibiting non-linearity. Our preliminary study reveals a very high degree of non-linearity from network traffic data, which explains why it is hard to improve the detection accuracy by using conventional machine learning techniques (e.g., SVM, Random Forest, Ad-aboosting). In this study, we empirically evaluate deep learning to see its feasibility for network anomaly detection. We examine a set of deep learning models constructed based on the Fully Connected Network (FCN), Variational AutoEncoder (VAE), and Long Short-Term Memory with Sequence to Sequence (LSTM Seq2Seq) structures, with two public traffic data sets that have distinctive properties with respect to the distribution of normal and attack populations. Our experimental results confirm the potential of deep learning models for network anomaly detection, and the model based on the LSTM Seq2Seq structure shows a highly promising performance, yielding 99% of binary classification accuracy on the public data sets.
Shamela: A Large-Scale Historical Arabic Corpus

Arabic is a widely-spoken language with a rich and long history spanning more than fourteen centuries. Yet existing Arabic corpora largely focus on the modern period or lack sufficient diachronic information. We develop a large-scale, historical corpus of Arabic of about 1 billion words from diverse periods of time. We clean this corpus, process it with a morphological analyzer, and enhance it by detecting parallel passages and automatically dating undated texts. We demonstrate its utility with selected case-studies in which we show its application to the digital humanities.
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation

It has been shown that news events influence the trends of stock price movements. However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation information, and hence cannot represent complete and exact events. Recent advances in Open Information Extraction (Open IE) techniques enable the extraction of structured events from web-scale data. We propose to adapt Open IE technology for event-based stock price movement prediction, extracting structured events from large-scale public news without manual efforts. Both linear and nonlinear models are employed to empirically investigate the hidden and complex relationships between events and the stock market. Largescale experiments show that the accuracy of S&P 500 index prediction is 60%, and that of individual stock prediction can be over 70%. Our event-based system outperforms bags-of-words-based baselines, and previously reported systems trained on S&P 500 stock historical data.