nils-herrmann/clean_scirepeval huggingface dataset:
Managing uncertainty in moving objects databases

This article addresses the problem of managing Moving Objects Databases (MODs) which capture the inherent imprecision of the information about the moving object's location at a given time. We deal systematically with the issues of constructing and representing the trajectories of moving objects and querying the MOD. We propose to model an uncertain trajectory as a three-dimensional (3D) cylindrical body and we introduce a set of novel but natural spatio-temporal operators which capture the uncertainty and are used to express spatio-temporal range queries. We devise and analyze algorithms for processing the operators and demonstrate that the model incorporates the uncertainty in a manner which enables efficient querying, thus striking a balance between the modeling power and computational efficiency. We address some implementation aspects which we experienced in our DOMINO project, as a part of which the operators that we introduce have been implemented. We also report on some experimental observations of a practical relevance.
Uses, traditional management, perception of variation and preferences in ackee (Blighia sapida K.D. Koenig) fruit traits in Benin: implications for domestication and conservation

Blighia sapida is a woody perennial multipurpose fruit tree species native to the Guinean forests of West Africa. The fleshy arils of the ripened fruits are edible. Seeds and capsules of the fruits are used for soap-making and all parts of the tree have medicinal properties. Although so far overlooked by researchers in the region, the tree is highly valued by farmers and is an important component of traditional agroforestry systems in Benin. Fresh arils, dried arils and soap are traded in local and regional markets in Benin providing substantial revenues for farmers, especially women. Recently, ackee has emerged as high-priority species for domestication in Benin but information necessary to elaborate a clear domestication strategy is still very sketchy. This study addresses farmers' indigenous knowledge on uses, management and perception of variation of the species among different ethnic groups taking into account also gender differences.



240 randomly selected persons (50% women) belonging to five different ethnic groups, 5 women active in the processing of ackee fruits and 6 traditional healers were surveyed with semi-structured interviews. Information collected refer mainly to the motivation of the respondents to conserve ackee trees in their land, the local uses, the perception of variation, the preference in fruits traits, the management practices to improve the production and regenerate ackee.



People have different interests on using ackee, variable knowledge on uses and management practices, and have reported nine differentiation criteria mainly related to the fruits. Ackee phenotypes with preferred fruit traits are perceived by local people to be more abundant in managed in-situ and cultivated stands than in unmanaged wild stands, suggesting that traditional management has initiated a domestication process. As many as 22 diseases have been reported to be healed with ackee. In general, indigenous knowledge about ackee varies among ethnic and gender groups.



With the variation observed among ethnic groups and gender groups for indigenous knowledge and preference in fruits traits, a multiple breeding sampling strategy is recommended during germplasm collection and multiplication. This approach will promote sustainable use and conservation of ackee genetic resources.
Exercise for posterior tibial tendon dysfunction: a systematic review of randomised clinical trials and clinical guidelines

To systematically review all randomised clinical trials to determine the efficacy of local strengthening exercises compared with other forms of conservative management for adults with posterior tibial tendon dysfunction.



Systematic review.



Four electronic databases (Cumulative Index to Nursing and Allied Health Literature, Cochrane, Embase and PubMed) were searched up to June 2018.



The study included randomised clinical trials investigating individuals with posterior tibial tendon dysfunction where local strengthening was compared with other forms of conservative management with respect to pain, function and/or physical impairment outcome measures. Standardised mean differences (SMDs) were used to compare change scores between groups and descriptors of exercise prescription assessed according to the Template for Intervention Description and Replication and the Toigo and Boutellier recommendations.



3 studies (n=93) were eligible for inclusion in the review. Varying strengthening exercises were compared with stretching and foot orthoses (n=2) or no intervention (n=1). Moderate effects (SMD 0.6-1.2) were found for reducing pain and disability with eccentric strengthening in conjunction with stretching and orthoses compared with concentric exercises, stretching and orthoses combined, and stretching and orthoses alone. Evaluation of exercise prescription parameters demonstrated minimal reporting, with the only consistent parameters being the number of sets and repetitions of the exercises, and the duration of the experimental period.



This review demonstrates the paucity of high-quality research for the conservative management of posterior tibial tendon dysfunction, and highlights the lack of exercise prescription parameters reported in clinical trials.
On Learning Causal Models from Relational Data

Many applications call for learning causal models from relational data. We investigate Relational Causal Models (RCM) under relational counterparts of adjacency-faithfulness and orientation-faithfulness, yielding a simple approach to identifying a subset of relational d-separation queries needed for determining the structure of an RCM using d-separation against an unrolled DAG representation of the RCM. We provide original theoretical analysis that offers the basis of a sound and efficient algorithm for learning the structure of an RCM from relational data. We describe RCD-Light, a sound and efficient constraint-based algorithm that is guaranteed to yield a correct partially-directed RCM structure with at least as many edges oriented as in that produced by RCD, the only other existing algorithm for learning RCM. We show that unlike RCD, which requires exponential time and space, RCDLight requires only polynomial time and space to orient the dependencies of a sparse RCM.
The virtual geographies of social networks: a comparative analysis of Facebook, LinkedIn and ASmallWorld

This study provided a comparative analysis of three social network sites, the open-to-all Facebook, the professionally oriented LinkedIn and the exclusive, members-only ASmallWorld.The analysis focused on the underlying structure or architecture of these sites, on the premise that it may set the tone for particular types of interaction.Through this comparative examination, four themes emerged, highlighting the private/public balance present in each social networking site, styles of self-presentation in spaces privately public and publicly private, cultivation of taste performances as a mode of sociocultural identification and organization and the formation of tight or loose social settings. Facebook emerged as the architectural equivalent of a glasshouse, with a publicly open structure, looser behavioral norms and an abundance of tools that members use to leave cues for each other. LinkedIn and ASmallWorld produced tighter spaces, which were consistent with the taste ethos of each network and offered less room for spontaneous interaction and network generation.
Closing the AI Knowledge Gap

AI researchers employ not only the scientific method, but also methodology from mathematics and engineering. However, the use of the scientific method – specifically hypothesis testing – in AI is typically conducted in service of engineering objectives. Growing interest in topics such as fairness and algorithmic bias show that engineering-focused questions only comprise a subset of the important questions about AI systems. This results in the AI Knowledge Gap: the number of unique AI systems grows faster than the number of studies that characterize these systems’ behavior. To close this gap, we argue that the study of AI could benefit from the greater inclusion of researchers who are well positioned to formulate and test hypotheses about the behavior of AI systems. We examine the barriers preventing social and behavioral scientists from conducting such studies. Our diagnosis suggests that accelerating the scientific study of AI systems requires new incentives for academia and industry, mediated by new tools and institutions. To address these needs, we propose a two-sided marketplace called TuringBox. On one side, AI contributors upload existing and novel algorithms to be studied scientifically by others. On the other side, AI examiners develop and post machine intelligence tasks designed to evaluate and characterize algorithmic behavior. We discuss this market’s potential to democratize the scientific study of AI behavior, and thus narrow the AI Knowledge Gap. 1 The Many Facets of AI Research Although AI is a sub-discipline of computer science, AI researchers do not exclusively use the scientific method in their work. For example, the methods used by early AI researchers often drew from logic, a subfield of mathematics, and are distinct from the scientific method we think of today. Indeed AI has adopted many techniques and approaches over time. In this section, we distinguish and explore the history of these ∗Equal contribution. methodologies with a particular emphasis on characterizing the evolving science of AI.
Expanding the mirror: vicarious activity for actions, emotions, and sensations

We often empathically share the states of others. The discovery of 'mirror neurons' suggested a neural mechanism for monkeys to share the actions of others. Here we expand this view by showing that mirror neurons for actions not only exist in the premotor cortex or in monkeys and that vicarious activity can also be measured for the emotions and sensations of others. Although we still need to empirically explore the function and development of these vicarious activations, we should stop thinking of vicarious brain activity as a peculiar property of the premotor cortex: instead it seems to be a very common phenomenon which leads social stimuli to recruit a wide range of seemingly private neural systems.
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks

Removing perspective distortion from hand held camera captured document images is one of the primitive tasks in document analysis, but unfortunately no such method exists that can reliably remove the perspective distortion from document images automatically. In this paper, we propose a convolutional neural network based method for recovering homography from hand-held camera captured documents. Our proposed method works independent of document’s underlying content and is trained end-to-end in a fully automatic way. Specifically, this paper makes following three contributions: firstly, we introduce a large scale synthetic dataset for recovering homography from documents images captured under different geometric and photometric transformations; secondly, we show that a generic convolutional neural network based architecture can be successfully used for regressing the corners positions of documents captured under wild settings; thirdly, we show that L1 loss can be reliably used for corners regression. Our proposed method gives state-of-the-art performance on the tested datasets, and has potential to become an integral part of document analysis pipeline.
Dissecting cellulitis (Perifolliculitis Capitis Abscedens et Suffodiens): a comprehensive review focusing on new treatments and findings of the last decade with commentary comparing the therapies and causes of dissecting cellulitis to hidradenitis suppurativa.

Dissecting cellulitis (DC) also referred to as to as perifolliculitis capitis abscedens et suffodiens (Hoffman) manifests with perifollicular pustules, nodules, abscesses and sinuses that evolve into scarring alopecia. In the U.S., it predominantly occurs in African American men between 20-40 years of age. DC also occurs in other races and women more rarely. DC has been reported worldwide. Older therapies reported effective include: low dose oral zinc, isotretinoin, minocycline, sulfa drugs, tetracycline, prednisone, intralesional triamcinolone, incision and drainage, dapsone, antiandrogens (in women), topical clindamycin, topical isotretinoin, X-ray epilation and ablation, ablative C02 lasers, hair removal lasers (800nm and 694nm), and surgical excision. Newer treatments reported include tumor necrosis factor blockers (TNFB), quinolones, macrolide antibiotics, rifampin, alitretinoin, metronidazole, and high dose zinc sulphate (135-220 mg TID). Isotretinoin seems to provide the best chance at remission, but the number of reports is small, dosing schedules variable, and the long term follow up beyond a year is negligible; treatment failures have been reported. TNFB can succeed when isotretinoin fails, either as monotherapy, or as a bridge to aggressive surgical treatment, but long term data is lacking. Non-medical therapies noted in the last decade include: the 1064 nm laser, ALA-PDT, and modern external beam radiation therapy. Studies that span more than 1 year are lacking. Newer pathologic hair findings include: pigmented casts, black dots, and "3D" yellow dots. Newer associations include: keratitis-ichthyosis-deafness syndrome, Crohn disease and pyoderma gangrenosum. Older associations include arthritis and keratitis. DC is likely a reaction pattern, as is shown by its varied therapeutic successes and failures. The etiology of DC remains enigmatic and DC is distinct from hidradenitis suppurativa, which is shown by their varied responses to therapies and their histologic differences. Like HS, DC likely involves both follicular dysfunction and an aberrant cutaneous immune response to commensal bacteria, such as coagulase negative staphylococci. The incidence of DC is likely under-reported. The literature suggests that now most cases of DC can be treated effectively. However, the lack of clinical studies regarding DC prevents full understanding of the disease and limits the ability to define a consensus treatment algorithm.
A Social Network Analysis Framework for Modeling Health Insurance Claims Data

Health insurance companies in Brazil have their data about claims organized having the view only for providers. In this way, they loose the physician view and how they share patients. Partnership between physicians can view as a fruitful work in most of the cases but sometimes this could be a problem for health insurance companies and patients, for example a recommendation to visit another physician only because they work in same clinic. e focus of the work is to beer understand physicians activities and how these activities are represented in the data. Our approach considers three aspects: the relationships among physicians, the relationships between physicians and patients, and the relationships between physicians and health providers. We present the results of an analysis of a claims database (detailing 18 months of activity) from a large health insurance company in Brazil. e main contribution presented in this paper is a set of models to represent: mutual referral between physicians, patient retention, and physician centrality in the health insurance network. Our results show the proposed models based on social network frameworks, extracted surprising insights about physicians from real health insurance claims data.
Social Networks IM Forensics: Encryption Analysis

Abstract—In most regards, the twenty-first century may not bring revolutionary changes in electronic messaging technology in terms of applications or protocols. Security issues that have long been a concern in messaging application are finally being solved using a variety of products. Web-based messaging systems are rapidly evolving the text-based conversation. The users have the right to protect their privacy from the eavesdropper, or other parties which interferes the privacy of the users for such purpose. The chatters most probably use the instant messages to chat with others for personal issue; in which no one has the right eavesdrop the conversation channel and interfere this privacy. This is considered as a non-ethical manner and the privacy of the users should be protected. The author seeks to identify the security features for most public instant messaging services used over the internet and suggest some solutions in order to encrypt the instant messaging over the conversation channel. The aim of this research is to investigate through forensics and sniffing techniques, the possibilities of hiding communication using encryption to protect the integrity of messages exchanged. Authors used different tools and methods to run the investigations. Such tools include Wireshark packet sniffer, Forensics Tool Kit (FTK) and viaForensic mobile forensic toolkit. Finally, authors will report their findings on the level of security that encryption could provide to instant messaging services.
Speech Emotion Recognition Using CNN

Deep learning systems, such as Convolutional Neural Networks (CNNs), can infer a hierarchical representation of input data that facilitates categorization. In this paper, we propose to learn affect-salient features for Speech Emotion Recognition (SER) using semi-CNN. The training of semi-CNN has two stages. In the first stage, unlabeled samples are used to learn candidate features by contractive convolutional neural network with reconstruction penalization. The candidate features, in the second step, are used as the input to semi-CNN to learn affect-salient, discriminative features using a novel objective function that encourages the feature saliency, orthogonality and discrimination. Our experiment results on benchmark datasets show that our approach leads to stable and robust recognition performance in complex scenes (e.g., with speaker and environment distortion), and outperforms several well-established SER features.
Smart Wheel-based Stair-climbing Robot

Smart aspects in development of a wheel-based robot which is capable to climb stairs autonomously are presented. Autonomy connotes the ability to climb stairs without any mechanical contact to environment except ground contact with wheels. A comparison of wheel-based to other known locomotion systems is given. Advantages of wheel-based systems are emphasized. The presented Stair Climber (SC) robot uses a rotated chassis whereat the number of contacts between wheel pairs and ground alternates between one and two. As consequence, the SC experiences discontinuous dynamics due to wheel-to-ground repeating contact activations and deactivations. Moreover, the continuous dynamics characteristics of the SC distinguish in different contact situations. Consequently, the SC model is of hybrid dynamics. Methodic aspects of the research and development of the SC are given in overview focusing on modeling and control design. The control design contains a strategy for stairclimbing which is realized as a sequence of different motion types. These motion types include motion along equilibrium points as well as motion in between equilibrium points crossing discrete state borders. Since the system is of nonlinear dynamics, feedback linearization techniques are applied. Specific for one of the SC states is underactuation which has to be considered in control design. Furthermore, one state transition is realized within the virtual constraints framework resulting in smooth wheel-to-ground contact activations. Typical results cover the presented SC. Development perspectives of existing SC are given.
Epi defined (ED) FinFET: An alternate device architecture for high mobility Ge channel integration in PMOSFET

Band to band tunneling (BTBT) is a major challenge in Ge FinFETs due to its smaller band gap. Narrow fin widths reduce BTBT due to quantum confinement (QC). However, Line Edge Roughness (LER) on narrower fins causes large VT variability. Previously, we have proposed an architecture named Epitaxially Defined (ED) FinFET to reduce VT variability due to LER wherein channel depletion is defined by low doped highly uniform epitaxy (thus named Epi Defined FinFET) (epi-thickness non uniformity<;2%) over a thick highly doped Si fin instead of lithography based patterning subject to LER (non-uniformity<;50% i.e. 2nm LER on a 4nm fin). In the present work, we propose integration of Ge into EDFinFET architecture in which Ge (or SiGe) is grown on top of Si fin. Proposed structure shows 10× reduction in LER based VT variability in comparison to FinFETs. Valence band QC in gate oxide/Ge/Si stack is used to control BTBT. Biaxial stress in thin Ge epitaxially grown on Si results in 27% higher ION. Thin Ge film required is lower than critical defect free thickness of Ge epitaxy on Si. Hence defect free Ge integration into FinFET architecture in enabled. We also show that EDFinFET can enable multiple VT just by the application of a bias at the body terminal.
A 660pW multi-stage temperature-compensated timer for ultra-low-power wireless sensor node synchronization

Recent work in ultra-low-power sensor platforms has enabled a number of new applications in medical, infrastructure, and environmental monitoring. Due to their limited energy storage volume, these sensors operate with long idle times and ultra-low standby power ranging from 10s of nW down to 100s of pW [1–2]. Since radio transmission is relatively expensive, even at the lowest reported power of 0.2mW , wireless communication between sensor nodes must be performed infrequently. Accurate measurement of the time interval between communication events (i.e. the synchronization cycle) is of great importance. Inaccuracy in the synchronization cycle time results in a longer period of uncertainty where sensor nodes are required to enable their radios to establish communication , quickly making radios dominate the energy budget. Quartz crystal oscillators and CMOS harmonic oscillators exhibit very small sensitivity to supply voltage and temperature  but cannot be used in the target application space since they operate at very high frequencies and exhibit power consumption that is several orders of magnitude larger (>300nW) than the needed idle power. A gate-leakage-based timer was proposed [5] that leveraged small gate leakage currents to achieve power consumption within the required budget (< 1nW). However, this timer incurs high RMS jitter (1400ppm) and temperature sensitivity (0.16%/ºC). A 150pW program-and-hold timer was proposed [6] to reduce temperature sensitivity but its drifting clock frequency limits its use for synchronization. The quality of a timer is not captured well by RMS jitter since it ignores the averaging of jitter over multiple timer clock periods in a single synchronization cycle. Instead, we propose the uncertainty in a single synchronization cycle of length T as new metric and use this synchronization uncertainty (SU) to evaluate different timer approaches. The timer period is a random variable X(n), with mean and sigma, μ and σ. Given a synchronization cycle time T, consisting of N timer periods, we define SU as the standard deviation of T as given by √T/μ × σ, assuming X(n) is Gaussian. Note that a smaller clock period increases N and results in more averaging and a lower SU with fixed jitter (σ/μ).