nils-herrmann/clean_scirepeval huggingface dataset:
Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing

The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems have been traditionally addressed within the context of sensor array signal processing, the recent trend is to apply compressive sensing (CS) due to its capability to estimate sparse support even with an insufficient number of snapshots, in which case classical array signal processing fails. However, CS guarantees the accurate recovery in a probabilistic manner, which often shows inferior performance in the regime where the traditional array signal processing approaches succeed. The apparent dichotomy between the probabilistic CS and deterministic sensor array signal processing has not been fully understood. The main contribution of the present article is a unified approach that revisits the link between CS and array signal processing first unveiled in the mid 1990s by Feng and Bresler. The new algorithm, which we call compressive MUSIC, identifies the parts of support using CS, after which the remaining supports are estimated using a novel generalized MUSIC criterion. Using a large system MMV model, we show that our compressive MUSIC requires a smaller number of sensor elements for accurate support recovery than the existing CS methods and that it can approach the optimal -bound with finite number of snapshots even in cases where the signals are linearly dependent.
Be aware to care : Public self-awareness leads to a reversal of the bystander effect

a Department of Social and Organizational Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands b Netherlands Institute for the Study of Crime and Law Enforcement, De Boelelaan 1077a, 1081 HV, Amsterdam, The Netherlands c Department of Criminal Law and Criminology, VU University Amsterdam, De Boelelaan 1077, 1081 HV, Amsterdam, The Netherlands d Phoolan Devi Institute, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
Pole placement techniques for a class of IIR blind equalizers

This work proposes a method for blind equalization of possibly non-minimum phase channels using particular infinite impulse response (IIR) filters. In this context, the transfer function of the equalizer is represented by a linear combination of specific rational basis functions. This approach estimates separately the coefficients of the linear expansion and the poles of the rational basis functions by alternating iteratively between an adaptive (fixed pole) estimation of the coefficients and a pole placement method. The focus of the work is mainly on the issue of good pole placement (initialization and updating).
Subjective and Objective Quality-of-Experience of Adaptive Video Streaming

With the rapid growth of streaming media applications, there has been a strong demand of Quality-of-Experience (QoE) measurement and QoE-driven video delivery technologies. While the new worldwide standard dynamic adaptive streaming over hypertext transfer protocol (DASH) provides an inter-operable solution to overcome the volatile network conditions, its complex characteristic brings new challenges to the objective video QoE measurement models. How streaming activities such as stalling and bitrate switching events affect QoE is still an open question, and is hardly taken into consideration in the traditionally QoE models. More importantly, with an increasing number of objective QoE models proposed, it is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this study, we build two subject-rated streaming video databases. The progressive streaming video database is dedicated to investigate the human responses to the combined effect of video compression, initial buffering, and stalling. The adaptive streaming video database is designed to evaluate the performance of adaptive bitrate streaming algorithms and objective QoE models. We also provide useful insights on the improvement of adaptive bitrate streaming algorithms. Furthermore, we propose a novel QoE prediction approach to account for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions between them. Twelve QoE algorithms from four categories including signal fidelity-based, network QoS-based, application QoSbased, and hybrid QoE models are assessed in terms of correlation with human perception
Deep Visual Domain Adaptation: A Survey

Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.
Ageing of the human metaorganism: the microbial counterpart

Human beings have been recently reviewed as ‘metaorganisms’ as a result of a close symbiotic relationship with the intestinal microbiota. This assumption imposes a more holistic view of the ageing process where dynamics of the interaction between environment, intestinal microbiota and host must be taken into consideration. Age-related physiological changes in the gastrointestinal tract, as well as modification in lifestyle, nutritional behaviour, and functionality of the host immune system, inevitably affect the gut microbial ecosystem. Here we review the current knowledge of the changes occurring in the gut microbiota of old people, especially in the light of the most recent applications of the modern molecular characterisation techniques. The hypothetical involvement of the age-related gut microbiota unbalances in the inflamm-aging, and immunosenescence processes will also be discussed. Increasing evidence of the importance of the gut microbiota homeostasis for the host health has led to the consideration of medical/nutritional applications of this knowledge through the development of probiotic and prebiotic preparations specific for the aged population. The results of the few intervention trials reporting the use of pro/prebiotics in clinical conditions typical of the elderly will be critically reviewed.
A lower-extremity exoskeleton improves knee extension in children with crouch gait from cerebral palsy

The ability to walk contributes considerably to physical health and overall well-being, particularly in children with motor disability, and is therefore prioritized as a rehabilitation goal. However, half of ambulatory children with cerebral palsy (CP), the most prevalent childhood movement disorder, cease to walk in adulthood. Robotic gait trainers have shown positive outcomes in initial studies, but these clinic-based systems are limited to short-term programs of insufficient length to maintain improved function in a lifelong disability such as CP. Sophisticated wearable exoskeletons are now available, but their utility in treating childhood movement disorders remains unknown. We evaluated an exoskeleton for the treatment of crouch (or flexed-knee) gait, one of the most debilitating pathologies in CP. We show that the exoskeleton reduced crouch in a cohort of ambulatory children with CP during overground walking. The exoskeleton was safe and well tolerated, and all children were able to walk independently with the device. Rather than guiding the lower limbs, the exoskeleton dynamically changed the posture by introducing bursts of knee extension assistance during discrete portions of the walking cycle, a perturbation that resulted in maintained or increased knee extensor muscle activity during exoskeleton use. Six of seven participants exhibited postural improvements equivalent to outcomes reported from invasive orthopedic surgery. We also demonstrate that improvements in crouch increased over the course of our multiweek exploratory trial. Together, these results provide evidence supporting the use of wearable exoskeletons as a treatment strategy to improve walking in children with CP.
The amygdala modulates the consolidation of memories of emotionally arousing experiences.

Converging findings of animal and human studies provide compelling evidence that the amygdala is critically involved in enabling us to acquire and retain lasting memories of emotional experiences. This review focuses primarily on the findings of research investigating the role of the amygdala in modulating the consolidation of long-term memories. Considerable evidence from animal studies investigating the effects of posttraining systemic or intra-amygdala infusions of hormones and drugs, as well as selective lesions of specific amygdala nuclei, indicates that (a) the amygdala mediates the memory-modulating effects of adrenal stress hormones and several classes of neurotransmitters; (b) the effects are selectively mediated by the basolateral complex of the amygdala (BLA); (c) the influences involve interactions of several neuromodulatory systems within the BLA that converge in influencing noradrenergic and muscarinic cholinergic activation; (d) the BLA modulates memory consolidation via efferents to other brain regions, including the caudate nucleus, nucleus accumbens, and cortex; and (e) the BLA modulates the consolidation of memory of many different kinds of information. The findings of human brain imaging studies are consistent with those of animal studies in suggesting that activation of the amygdala influences the consolidation of long-term memory; the degree of activation of the amygdala by emotional arousal during encoding of emotionally arousing material (either pleasant or unpleasant) correlates highly with subsequent recall. The activation of neuromodulatory systems affecting the BLA and its projections to other brain regions involved in processing different kinds of information plays a key role in enabling emotionally significant experiences to be well remembered.
The mass culture of Dunaliella salina for fine chemicals: From laboratory to pilot plant

Considerations of microalgae as a source offood and biochemicals began in the early 1940's, and in 1952 the first Algae Mass-Culture Symposium was held (Burlew, 1953). Since then, a number of microalgae have been suggested and evaluated for their suitability for commercial exploitation. These include Chlorella, Scenedesmus and Spirulina (e.g., Soeder, 1976; Kawaguchi, 1980; Becker & Venkataraman, 1980) and small commercial operations culturing some of these algae for food are underway in various parts of the world. The extremely halophilic unicellular green alga Dunaliella salina (Chlorophyta, Volvocales) has been proposed as a source of its osmoregulatory solute, glycerol and the pigment f3-carotene (Masyuk, 1968; Aasen, et a11969; Ben-Amotz & A vron, 1980). Much research on the commercial potential of this algae and its products has been undertaken (e.g., Williams, et al. 1978; Chen & Chi, 1981) and trial operations have been established in the USSR (Masyuk, 1968) and in Israel (Ben-Amotz & A vron, 1980). Since 1978, we in Australia have been working also, to examine the feasibility of using large-scale culture of Dunaliella salina as a commercial source
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages

Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on the assisting-target language pair (parent model) which is later fine-tuned for the sourcetarget language pair of interest (child model), with the target language being the same. In many cases, the assisting language has a different word order from the source language. We show that divergent word order adversely limits the benefits from transfer learning when little to no parallel corpus between the source and target language is available. To bridge this divergence, We propose to pre-order the assisting language sentence to match the word order of the source language and train the parent model. Our experiments on many language pairs show that bridging the word order gap leads to significant improvement in the translation quality.
Spinal canal remodeling in burst fractures of the thoracolumbar spine: a computerized tomographic comparison between operative and nonoperative treatment.

Operative and nonoperative treatment methods of burst fractures were compared regarding canal remodeling. The entire series consisted of 18 patients, with seven in the operative treatment group and 11 in the nonoperative treatment group. All fractures were studied with computed tomography (CT) at the postoperative (operative treatment group) or postinjury (nonoperative treatment group) and the latest follow-up. All patients were followed up for > or = 18 months. There was no statistical difference between postoperative and postinjury canal areas (p = 0.0859). However, a significant difference was found between the rates of remodeling (p = 0.0059). Although spinal canal remodeling occurred in both groups, the resorption of retropulsed fragments was less favorable in nonoperative treatment group.
A two-step system for direct bank telemarketing outcome classification

Intell Sys Acc Fin Mgmt. 2017;24:49–55.  A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐ step system presented performs better than all its individual components. In addition, the two‐ step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.
DROL: An Object-Oriented Programming Language for Distributed Real-Time Systems

In this paper, we first characterize distributed real-time systems by the following two properties that have to be supported: best eflorl and leas2 suffering. Then, we propose a distributed real-time object model DRO which complies these properties. Based on the DRO model, we design an object oriented programming language DROL: an extension of C++ with the capa.bility of describing distributed real-time systems. The most eminent feature of DROL is that users can describe on meta level the semantics of message communications as a communication protocol with sending and receiving primitives. With this feature, we can construct a flexible distributed real-time system satisfying specifications which include timing constraints. We implement a runtime system of DROL on the ARTS kernel, and evaluate the efficiency of the prototype implementation as well as confirm the high expressive power of the language.
A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs

Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as “solitary white nodule-like blob.” However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed. We employed stationary wavelet transform and convergence index filter to extract the texture features and used AdaBoost to generate white nodule-likeness map. A solitary feature was defined to evaluate the isolation degree of candidates. Both the isolation degree and the white nodule likeness were used as final evaluation of lung nodule candidates. The proposed method shows better performance and robustness than those reported in previous research. More than 80% and 93% of lung nodules in the lung field in the Japanese Society of Radiological Technology (JSRT) database were detected when the false positives per image were two and five, respectively. The proposed approach has the potential of being used in clinical practice.
Authorship Attribution Using Word Sequences

Authorship attribution is the task of identifying the author of a given text. The main concern of this task is to define an appropriate characterization of documents that captures the writing style of authors. This paper proposes a new method for authorship attribution supported on the idea that a proper identification of authors must consider both stylistic and topic features of texts. This method characterizes documents by a set of word sequences that combine functional and content words. The experimental results on poem classification demonstrated that this method outperforms most current state-of-the-art approaches, and that it is appropriate to handle the attribution of short documents.
Principles of survey research: part 1: turning lemons into lemonade

Surveys are probably the most commonly-used research method world-wide. Survey work is visible not only because we see many examples of it in software engineering research, but also because we are often asked to participate in surveys in our private capacity, as electors, consumers, or service users. This widespread use of surveys may give us the impression that surveybased research is straightforward, an easy option for researchers to gather important information about products, context, processes, workers and more. In our personal experience with applying and evaluating research methods and their results, we certainly did not expect to encounter major problems with a survey that we planned, to investigate issues associated with technology adoption. This article and subsequent ones in this series describe how wrong we were. We do not want to give the impression that there is any way of turning a bad survey into a good one; if a survey is a lemon, it stays a lemon. However, we believe that learning from our mistakes is the way to make lemonade from lemons. So this series of articles shares with you our lessons learned, in the hope of improving survey research in software engineering.
CoC: Secure Supply Chain Management System Based on Public Ledger

Modern supply chain is a complex system and plays an important role for different sectors under the globalization economic integration background. Supply chain management system is proposed to handle the increasing complexity and improve the efficiency of flows of goods. It is also useful to prevent potential frauds and guarantee trade compliance. Currently, most companies maintain their own IT system for supply chain management. However, this approach has some limitations that prevent one to get most of the supply chain information. Using emerging decentralized ledger technology to build supply chain management system is a promising direction. However, decentralized ledger usually suffers from low performance and lack of capability to protect information stored on the ledger. To overcome these challenges, we propose CoC, a novel supply chain management system based on hybrid decentralized ledger. We develop an efficient block construction method with the model and security mechanism to prevent unauthorized access to data stored on the ledger.
Information visualization- in regarding to customer journey map in a three-dimensions format

Customer journey map used in the service design was considered an important tool to discover service experiences related issues, since it could logically and contextually find problems and solves them. This article tried to conduct the idea of information visualization. and utilized the customer journey map in a three-dimensions format; therefore, designers not only can acquire information from 2D diagram, but also manipulate intangible information from this designed tool. Through 3 service design case studies, this article expects to develop various application tools, which allowing designers explore more opportunities gaps from users’ experiences.
Clustering the solar resource for grid management in island mode

We propose a novel methodology to select candidate locations for solar power plants that take into account solar variability and geographical smoothing effects. This methodology includes the development of maps created by a clustering technique that determines regions of coherent solar quality attributes as defined by a feature which considers both solar clearness and solar variability. An efficient combination of two well-known clustering algorithms, the affinity propagation and the k-means, is introduced in order to produce stable partitions of the data to a variety of number of clusters in a computationally fast and reliable manner. We use 15 years worth of the 30min GHI gridded data for the island of Lanai in Hawaii to produce, validate and reproduce clustering maps. A family of appropriate number of clusters is obtained by evaluating the performance of three internal validity indices. We apply a correlation analysis to the family of solutions to determine the map segmentation that maximizes a definite interpretation of the distinction between and within the emerged clusters. Having selected a single clustering we validated the clustering by using a new dataset to demonstrate that the degree of similarity between the two partitions remains high at 90.91%. In the end we show how the clustering map can be used in solar energy problems. Firstly, we explore the effects of geographical smoothing in terms of the clustering maps, by determining the average ramp ratio for two location within and without the same cluster and identify the pair of clusters that shows the highest smoothing potential. Secondly, we demonstrate how the map can be used to select locations for GHI measurements to improve solar forecasting for a PV plant, by showing that additional measurements from within the cluster where the PV plant is located can lead to improvements of 10% in the forecast.