nils-herrmann/clean_scirepeval huggingface dataset:
Stock Optimization of Spare Parts with Genetic Algorithm

In this research, a new method is proposed for the optimization of warship spare parts stock with genetic algorithm. Warships should fulfill her duties in all circumstances. Considering the warships have more than a hundred thousand unique parts, it is a very hard problem to decide which spare parts should be stocked at warehouse aiming to use in case of failure. In this study, genetic algorithm that is a heuristic optimization method is used to solve this problem. The demand quantity, the criticality and the cost of parts is used for optimization. A genetic algorithm with very long chromosome is used, i.e. over 1000 genes in one chromosome. The outputs of the method is analyzed and compared with the Price Sensitive 0.5 FLSIP+ model, which is widely used over navies, and came to a conclusion that the proposed method is better.
Explaining Recommendations Using Contexts

Recommender systems support user decision-making, and explanations of recommendations further facilitate their usefulness. Previous explanation styles are based on similar users, similar items, demographics of users, and contents of items. Contexts, such as usage scenarios and accompanying persons, have not been used for explanations, although they influence user decisions. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming recommended items. The expected impacts of context style explanations are 1) persuasiveness: recognition of suitable context for usage motivates users to consume items, and 2) usefulness: envisioning context helps users to make right choices because the values of items depend on contexts. We evaluate context style persuasiveness and usefulness by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to demographic and content style explanations. We also combine context style and other explanation styles, confirming that hybrid styles improve persuasiveness and usefulness of explanation.
Circulating Tumor Cells (CTC) and Cell-Free DNA (cfDNA) Workshop 2016: Scientific Opportunities and Logistics for Cancer Clinical Trial Incorporation

Despite the identification of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) as potential blood-based biomarkers capable of providing prognostic and predictive information in cancer, they have not been incorporated into routine clinical practice. This resistance is due in part to technological limitations hampering CTC and cfDNA analysis, as well as a limited understanding of precisely how to interpret emergent biomarkers across various disease stages and tumor types. In recognition of these challenges, a group of researchers and clinicians focused on blood-based biomarker development met at the Canadian Cancer Trials Group (CCTG) Spring Meeting in Toronto, Canada on 29 April 2016 for a workshop discussing novel CTC/cfDNA technologies, interpretation of data obtained from CTCs versus cfDNA, challenges regarding disease evolution and heterogeneity, and logistical considerations for incorporation of CTCs/cfDNA into clinical trials, and ultimately into routine clinical use. The objectives of this workshop included discussion of the current barriers to clinical implementation and recent progress made in the field, as well as fueling meaningful collaborations and partnerships between researchers and clinicians. We anticipate that the considerations highlighted at this workshop will lead to advances in both basic and translational research and will ultimately impact patient management strategies and patient outcomes.
How Gamification Affects Physical Activity: Large-scale Analysis of Walking Challenges in a Mobile Application

Gamification represents an effective way to incentivize user behavior across a number of computing applications. However, despite the fact that physical activity is essential for a healthy lifestyle, surprisingly little is known about how gamification and in particular competitions shape human physical activity. Here we study how competitions affect physical activity. We focus on walking challenges in a mobile activity tracking application where multiple users compete over who takes the most steps over a predefined number of days. We synthesize our findings in a series of game and app design implications. In particular, we analyze nearly 2,500 physical activity competitions over a period of one year capturing more than 800,000 person days of activity tracking. We observe that during walking competitions, the average user increases physical activity by 23%. Furthermore, there are large increases in activity for both men and women across all ages, and weight status, and even for users that were previously fairly inactive. We also find that the composition of participants greatly affects the dynamics of the game. In particular, if highly unequal participants get matched to each other, then competition suffers and the overall effect on the physical activity drops significantly. Furthermore, competitions with an equal mix of both men and women are more effective in increasing the level of activities. We leverage these insights to develop a statistical model to predict whether or not a competition will be particularly engaging with significant accuracy. Our models can serve as a guideline to help design more engaging competitions that lead to most beneficial behavioral changes.
Fillers for the improvement in acne scars

Acne is a common inflammatory disease. Scarring is an unwanted end point of acne. Both atrophic and hypertrophic scar types occur. Soft-tissue augmentation aims to improve atrophic scars. In this review, we will focus on the use of dermal fillers for acne scar improvement. Therefore, various filler types are characterized, and available data on their use in acne scar improvement are analyzed.
A Model for Interactive CSR Campaigns using Storytelling

Companies today are expected to engage in corporate social responsibility (CSR) and they spend a lot of time, money, and other resources on these tasks. However, in relation to their investment, the gain for most companies is marginal, because their efforts are only perceived by a small number of people. In this paper, our goal is to improve on this situation by involving a greater number of people in CSR campaigns and increasing media attention, while reducing expenses. We propose a model that utilizes storytelling on alternate realities to link social media with CSR tasks. Consumers are engaged in a story through interactive storytelling interfaces, which allow them to contribute to the CSR campaign. The company is always able to monitor and control their running campaign and can profit from social media contributions that spread the campaign goals. We describe the capabilities of the model as well as the problems it faces with storytelling on huge numbers of automatically extracted stories. The main challenge of the kind of storytelling we report is to find an adequate storytelling structure for an automatically generated story.
A brain in a vat cannot break out : why the singularity must be extended , embedded and embodied

The present paper criticizes Chalmers's discussion of the Singularity, viewed as the emergence of a superhuman intelligence via the self-amplifying development of artificial intelligence. The situated and embodied view of cognition rejects the notion that intelligence could arise in a closed "brain-in-a-vat" system, because intelligence is rooted in a high-bandwidth, sensory-motor interaction with the outside world. Instead, it is proposed that superhuman intelligence can emerge only in a distributed fashion, in the form of a self-organizing network of humans, computers, and other technologies: the "Global Brain".
Lung Nodule Detection via Deep Reinforcement Learning

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.
Duet: An OLED & GPU Co-management Scheme for Dynamic Resolution Adaptation

The increasingly high display resolution of mobile devices imposes a further burden on energy consumption. Existing schemes manage either OLED or GPU power to save energy. This paper presents the design, algorithm, and implementation of a co-managing scheme called Duet, which automatically trades off perceptual quality for energy efficiency in accordance with static and dynamic visual acuity when users interact with mobile applications. The results of experiments conducted on a commercial smartphone with popular interactive apps show that Duet saves more energy while retaining better visual quality, compared with a joint scheme that simultaneously uses dynamic pixel disabling and dynamic resolution scaling to save OLED and GPU energy in isolation.
Endoscopic diagnosis of laryngeal cancer and precancerous lesions by narrow band imaging.

To investigate the characteristics of the laryngeal mucosal microvascular network in suspected laryngeal cancer patients, using narrow band imaging, and to evaluate the value of narrow band imaging endoscopy in the early diagnosis of laryngeal precancerous and cancerous lesions.



Eighty-five consecutive patients with suspected precancerous or cancerous laryngeal lesions were enrolled in the study. Endoscopic narrow band imaging findings were classified into five types (I to V) according to the features of the mucosal intraepithelial papillary capillary loops assessed.



A total of 104 lesions (45 malignancies and 59 nonmalignancies) was detected under white light and narrow band imaging modes. The sensitivity and specificity of narrow band imaging in detecting malignant lesions were 88.9 and 93.2 per cent, respectively. The intraepithelial papillary capillary loop classification, as determined by narrow band imaging, was closely associated with the laryngeal lesions' histological findings. Type I to IV lesions were considered nonmalignant and type V lesions malignant. For type Va lesions, the sensitivity and specificity of narrow band imaging in detecting severe dysplasia or carcinoma in situ were 100 and 79.5 per cent, respectively. In patients with type Vb and Vc lesions, the sensitivity and specificity of narrow band imaging in detecting invasive carcinoma were 83.8 and 100 per cent, respectively.



Narrow band imaging is a promising approach enabling in vivo differentiation of nonmalignant from malignant laryngeal lesions by evaluating the morphology of mucosal capillaries. These results suggest endoscopic narrow band imaging may be useful in the early detection of laryngeal cancer and precancerous lesions.
Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry.

The purpose of this investigation was to determine whether force platform measurements can be used to objectively assess short-term effects of spinal manipulation on patients with diagnosed, chronic unilateral "sacroiliac dyskinesia," here defined as decreased interarticular mobility of the sacroiliac joint. Nine patients walked across a force platform, were than manipulated by a chiropractor and then repeated the gait trials. Temporal and kinetic gait variables from the force platform measurements were analyzed for changes in the symmetry of the subjects' gait before and after treatment sessions. There was a distinct tendency towards improved gait symmetry after treatment in those cases where the gait was asymmetric prior to the treatment. This result indicated that force platform measurements may be used successfully to assess the effects of spinal manipulations on the gait of patients with sacroiliac dyskinesia.
AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks

In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the Nature-inspired Ant Colony Optimization framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet’s performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks.
A Hybrid Approach for Movie Recommendation System Using Feature Engineering

Recommender system is used to recommend items and services to the users and provide recommendations based on prediction. The prediction performance plays vital role in the quality of recommendation. To improve the prediction performance, this paper proposed a new hybrid method based on naïve Bayesian classifier with Gaussian correction and feature engineering. The proposed method is experimented on the well known movie lens 100k data set. The results show better results when compared with existing methods.
DeepIM: Deep Iterative Matching for 6D Pose Estimation

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the input image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over stateof-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.
Memory functions in chronic pain: examining contributions of attention and age to test performance.

Previous studies have revealed that memory performance is diminished in chronic pain patients. Few studies, however, have assessed multiple components of memory in a single sample. It is currently also unknown whether attentional problems, which are commonly observed in chronic pain, mediate the decline in memory. Finally, previous studies have focused on middle-aged adults, and a possible detrimental effect of aging on memory performance in chronic pain patients has been commonly disregarded. This study, therefore, aimed at describing the pattern of semantic, working, and visual and verbal episodic memory performance in participants with chronic pain, while testing for possible contributions of attention and age to task performance.



Thirty-four participants with chronic pain and 32 pain-free participants completed tests of episodic, semantic, and working memory to assess memory performance and a test of attention.



Participants with chronic pain performed worse on tests of working memory and verbal episodic memory. A decline in attention explained some, but not all, group differences in memory performance. Finally, no additional effect of age on the diminished task performance in participants with chronic pain was observed.



Taken together, the results indicate that chronic pain significantly affects memory performance. Part of this effect may be caused by underlying attentional dysfunction, although this could not fully explain the observed memory decline. An increase in age in combination with the presence of chronic pain did not additionally affect memory performance.
Expressing emotion in text-based communication

Our ability to express and accurately assess emotional states is central to human life. The present study examines how people express and detect emotions during text-based communication, an environment that eliminates the nonverbal cues typically associated with emotion. The results from 40 dyadic interactions suggest that users relied on four strategies to express happiness versus sadness, including disagreement, negative affect terms, punctuation, and verbosity. Contrary to conventional wisdom, communication partners readily distinguished between positive and negative valence emotional communicators in this text-based context. The results are discussed with respect to the Social Information Processing model of strategic relational adaptation in mediated communication.
Influencing group participation with a shared display

During face-to-face interactions, groups frequently overly rely on the dominant viewpoint to lead the group in its decision-making process. We begin with a discussion of this phenomenon and the possibility for technology to assist in addressing it. We then present findings from a behavioral study that examines how a shared display of individual speaker-participation rates can impact the behavior of the group during a collaboration task. The results from the study indicate that the presence of such a display influences the behavior of group participants in the extremes of over and under participation. While influencing the quantity of time someone speaks is not directly equivalent to influencing the topics discussed, we suggest that this approach of providing peripheral displays of social information is promising for improving certain types of group interactions.
Remote Sensing and Geosciences for Archaeology

Archaeological remote sensing is not a novel discipline. Indeed, there is already a suite of geoscientific techniques that are regularly used by practitioners in the field, according to standards and best practice guidelines. However, (i) the technological development of sensors for data capture; (ii) the accessibility of new remote sensing and Earth Observation data; and (iii) the awareness that a combination of different techniques can lead to retrieval of diverse and complementary information to characterize landscapes and objects of archaeological value and significance, are currently three triggers stimulating advances in methodologies for data acquisition, signal processing, and the integration and fusion of extracted information. The Special Issue “Remote Sensing and Geosciences for Archaeology” therefore presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. Site discovery, understanding of cultural landscapes, augmented knowledge of heritage, condition assessment, and conservation are the main research and practice targets that the papers published in this Special Issue aim to address.
Genetics and intelligence differences: five special findings

Intelligence is a core construct in differential psychology and behavioural genetics, and should be so in cognitive neuroscience. It is one of the best predictors of important life outcomes such as education, occupation, mental and physical health and illness, and mortality. Intelligence is one of the most heritable behavioural traits. Here, we highlight five genetic findings that are special to intelligence differences and that have important implications for its genetic architecture and for gene-hunting expeditions. (i) The heritability of intelligence increases from about 20% in infancy to perhaps 80% in later adulthood. (ii) Intelligence captures genetic effects on diverse cognitive and learning abilities, which correlate phenotypically about 0.30 on average but correlate genetically about 0.60 or higher. (iii) Assortative mating is greater for intelligence (spouse correlations ~0.40) than for other behavioural traits such as personality and psychopathology (~0.10) or physical traits such as height and weight (~0.20). Assortative mating pumps additive genetic variance into the population every generation, contributing to the high narrow heritability (additive genetic variance) of intelligence. (iv) Unlike psychiatric disorders, intelligence is normally distributed with a positive end of exceptional performance that is a model for ‘positive genetics’. (v) Intelligence is associated with education and social class and broadens the causal perspectives on how these three inter-correlated variables contribute to social mobility, and health, illness and mortality differences. These five findings arose primarily from twin studies. They are being confirmed by the first new quantitative genetic technique in a century—Genome-wide Complex Trait Analysis (GCTA)—which estimates genetic influence using genome-wide genotypes in large samples of unrelated individuals. Comparing GCTA results to the results of twin studies reveals important insights into the genetic architecture of intelligence that are relevant to attempts to narrow the ‘missing heritability’ gap.
Interference analysis: From 2G to 4G

Unfortunately, better throughputs are continuously requested mobile networks face spectral resource scarcity. Consequently, spectrum resource's use must be optimized for that operators have introduced frequency reuse mechanism. Unfortunately, this mechanism is limited by co-channel interference and adjacent channel interference. An Orthogonal Frequency Division Multiple Access (OFDMA) has been introduced which was also better enhanced by femto cells concept. From this work it can be deduced that the Signal to co-channel interference ratio power does not exceed 14.1 dB for a user in edge cell whereas it can achieved 15.3 dB for a user in center cell with the same path loss value (γ=3.5). The signal to adjacent channel interference power ratio decreases from 20.7 w to 6.9 w if the number of active users in the neighbor interfering cell is increased from 10 to 30. However, from the fourth generation's study, we deduce that macro user's performances are reversely varied with femto cells number. In fact, SINR decreases from 10 dB to −5 dB if we increase the number of femto cells from 1 to 7.