nils-herrmann/clean_scirepeval huggingface dataset:
Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics.



The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis.



The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts.



We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression.



The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.
Tangible 3D tabletops: combining tangible tabletop interaction and 3D projection

In this paper we present the tangible 3D tabletop and discuss the design potential of this novel interface. The tangible 3D tabletop combines tangible tabletop interaction with 3D projection in such a way that the tangible objects may be augmented with visual material corresponding to their physical shapes, positions, and orientation on the tabletop. In practice, this means that both the tabletop and the tangibles can serve as displays. We present the basic design principles for this interface, particularly concerning the interplay between 2D on the tabletop and 3D for the tangibles, and present examples of how this kind of interface might be used in the domain of maps and geolocalized data. We then discuss three central design considerations concerning 1) the combination and connection of content and functions of the tangibles and tabletop surface, 2) the use of tangibles as dynamic displays and input devices, and 3) the visual effects facilitated by the combination of the 2D tabletop surface and the 3D tangibles.
Quantum algorithms for computing short discrete logarithms and factoring RSA integers

In this paper we generalize the quantum algorithm for computing short discrete logarithms previously introduced by Eker̊a  so as to allow for various tradeoffs between the number of times that the algorithm need be executed on the one hand, and the complexity of the algorithm and the requirements it imposes on the quantum computer on the other hand. Furthermore, we describe applications of algorithms for computing short discrete logarithms. In particular, we show how other important problems such as those of factoring RSA integers and of finding the order of groups under side information may be recast as short discrete logarithm problems. This immediately gives rise to an algorithm for factoring RSA integers that is less complex than Shor’s general factoring algorithm in the sense that it imposes smaller requirements on the quantum computer. In both our algorithm and Shor’s algorithm, the main hurdle is to compute a modular exponentiation in superposition. When factoring an n bit integer, the exponent is of length 2n bits in Shor’s algorithm, compared to slightly more than n/2 bits in our algorithm.
Some perspectives on deinstitutionalization.

The authors discuss what can be learned from our experience with deinstitutionalization. The deinstitutionalization of mentally ill persons has three components: the release of these individuals from hospitals into the community, their diversion from hospital admission, and the development of alternative community services. The greatest problems have been in creating adequate and accessible community resources. Where community services have been available and comprehensive, most persons with severe mental illness have significantly benefited. On the other hand, there have been unintended consequences of deinstitutionalization-a new generation of uninstitutionalized persons who have severe mental illness, who are homeless, or who have been criminalized and who present significant challenges to service systems. Among the lessons learned from deinstitutionalization are that successful deinstitutionalization involves more than simply changing the locus of care; that service planning must be tailored to the needs of each individual; that hospital care must be available for those who need it; that services must be culturally relevant; that severely mentally ill persons must be involved in their service planning; that service systems must not be restricted by preconceived ideology; and that continuity of care must be achieved.
Bringing Technological Frames to Work : How Previous Experience with Social Media Shapes the Technology ’ s Meaning in an Organization

This study examines the expectations that workers have regarding enterprise social media (ESM). Using interviews with 58 employees at an organization implementing an ESM platform, we compare workers’ views of the technology with those of existing workplace communication technologies and publicly available social media. We find individuals’ frames regarding expectations and assumptions of social media are established through activities outside work settings and influence employees’ views about the usefulness of ESM. Differences in technological frames regarding ESM were related to workers’ age and level of personal social media use, but in directions contrary to expectations expressed in the literature. Findings emphasize how interpretations of technology may shift over time and across contexts in unique ways for different individuals.
Position of human blood group O(H) and phenotype-determining enzymes in growth and infectious disease.

The human ABO(H) blood group phenotypes arise from the evolutionarily oldest genetic system found in primate populations. While the blood group antigen A is considered the ancestral primordial structure, under the selective pressure of life-threatening diseases blood group O(H) came to dominate as the most frequently occurring blood group worldwide.  phenotypes demonstrate impaired formation of adaptive and innate immunoglobulin specificities due to clonal selection and phenotype formation in plasma proteins. Compared with individuals with blood group O(H), blood group A individuals not only have a significantly higher risk of developing certain types of cancer but also exhibit high susceptibility to malaria tropica or infection by Plasmodium falciparum. The phenotype-determining blood group A glycotransferase(s), which affect the levels of anti-A/Tn cross-reactive immunoglobulins in phenotypic glycosidic accommodation, might also mediate adhesion and entry of the parasite to host cells via trans-species O-GalNAc glycosylation of abundantly expressed serine residues that arise throughout the parasite's life cycle, while excluding the possibility of antibody formation against the resulting hybrid Tn antigen. In contrast, human blood group O(H), lacking this enzyme, is indicated to confer a survival advantage regarding the overall risk of developing cancer, and individuals with this blood group rarely develop life-threatening infections involving evolutionarily selective malaria strains.
Performances of Probabilistic Caching Strategies in Content Centric Networking

Information centric network (ICN) is progressively becoming the revolutionary paradigm to the traditional Internet with improving data (content) distribution on the Internet along with global unique names. Some ICN-based architecture, such as named data network (NDN) and content centric network (CCN) has recently been developed to deal with prominent advantages to implement the basic idea of ICN. To improve the Internet services, its architecture design is shifting from host-centric (end-to-end) communication to receive-driven content retrieval. A prominent advantage of this novel architecture is that networks are equipped with transparent in-network caching to accelerate the content dissemination and improve the utilization of network resources. The gigantic increase of global network traffic poses new challenges to CCN caching technologies. It requires extensive flexibility for consumers to get information. One of the most imperative commonalities of CCN design is ubiquitous caching. It is broadly accepted that the in-network caching would improve the performance. ICN cache receives on several new characteristics: cache is ubiquitous, cache is transparent to application, and content to be cached is more significant. This paper presents a complete survey of state-of-art CCN-based probabilistic caching schemes aiming to address the caching issues, with certain focus on minimizing cache redundancy and improving the accessibility of cached content.
QR code security

This paper examines QR Codes and how they can be used to attack both human interaction and automated systems. As the encoded information is intended to be machine readable only, a human cannot distinguish between a valid and a maliciously manipulated QR code. While humans might fall for phishing attacks, automated readers are most likely vulnerable to SQL injections and command injections. Our contribution consists of an analysis of the QR Code as an attack vector, showing different attack strategies from the attackers point of view and exploring their possible consequences.
Algorithmes Génétiques et autres méthodes d'optimisation appliqués à la gestion de trafic aérien

This document discribes different optimization methods applied to the air traffic management domain. The first part details genetic algorithms and introduces a crossover operator adapted to partially separated problems. The operator is tested on an airport ground traffic optimization problem. In the second part, the conflict resolution problem is optimized with different (centralized or autonomous) models and different algorithms : genetic algorithms, branch and bound, neural networks, semidefinite programming, hybridization of genetic algorithms and deterministic methods such as linear programming or A∗ algorithms.
Brain structures and functional connectivity associated with individual differences in Internet tendency in healthy young adults

Internet addiction (IA) incurs significant social and financial costs in the form of physical side-effects, academic and occupational impairment, and serious relationship problems. The majority of previous studies on Internet addiction disorders (IAD) have focused on structural and functional abnormalities, while few studies have simultaneously investigated the structural and functional brain alterations underlying individual differences in IA tendencies measured by questionnaires in a healthy sample. Here we combined structural (regional gray matter volume, rGMV) and functional (resting-state functional connectivity, rsFC) information to explore the neural mechanisms underlying IAT in a large sample of 260 healthy young adults. The results showed that IAT scores were significantly and positively correlated with rGMV in the right dorsolateral prefrontal cortex (DLPFC, one key node of the cognitive control network, CCN), which might reflect reduced functioning of inhibitory control. More interestingly, decreased anticorrelations between the right DLPFC and the medial prefrontal cortex/rostral anterior cingulate cortex (mPFC/rACC, one key node of the default mode network, DMN) were associated with higher IAT scores, which might be associated with reduced efficiency of the CCN and DMN (e.g., diminished cognitive control and self-monitoring). Furthermore, the Stroop interference effect was positively associated with the volume of the DLPFC and with the IA scores, as well as with the connectivity between DLPFC and mPFC, which further indicated that rGMV variations in the DLPFC and decreased anticonnections between the DLPFC and mPFC may reflect addiction-related reduced inhibitory control and cognitive efficiency. These findings suggest the combination of structural and functional information can provide a valuable basis for further understanding of the mechanisms and pathogenesis of IA.
An inductorless asymmetrical ZVS full bridge converter for step-up applications with wide input voltage range

This paper proposes an inductorless full-bridge DC-DC converter for step up applications. The proposed converter can achieve ZVS of all switches by utilizing transformer leakage inductance and MOSFET output capacitance. Owing to negligible duty cycle loss and use of voltage doubler, the proposed converter has greatly reduced turn ratio, which makes this voltage-fed converter viable for high step up application. In addition, the proposed converter does not necessitate a clamp circuit at the secondary since the diodes are turned off under ZCS. The diode voltage rating is fixed at the output voltage regardless of input voltage. Further, the transformer VA rating is reduced compared to the conventional converter due to the absence of circulating current. Therefore, the proposed converter is suitable for application with wide input voltage range. Experimental results on a 1kW prototype are provided to validate the proposed concept.
Introduction to Cross Laminated Timber

Cross-laminated timber (CLT), a new generation of engineered wood product developed initially in Europe, has been gaining popularity in residential and non-residential applications in several countries. Numerous impressive lowand mid-rise buildings built around the world using CLT showcase the many advantages that this product can offer to the construction sector. This article provides basic information on the various attributes of CLT as a product and as structural system in general, and examples of buildings made of CLT panels. A road map for codes and standards implementation of CLT in North America is included, along with an indication of some of the obstacles that can be expected.
Fully Distributed Scrum: Linear Scalability of Production between San Francisco and India

The Scrum software development framework was designed for the hyperproductive state where productivity increases by 5-10 times over waterfall teams and many co-located teams have achieved this effect. In 2006, Xebia (The Netherlands) started localized projects with half Dutch and half Indian team members. After establishing a localized velocity of five times their waterfall competitors on the same project, they moved the Indian members of the team to India and showed stable velocity with fully distributed teams. The ability to achieve hyperproductivity with distributed, outsourced teams was shown to be a repeatable process and a fully distributed model is now the recommended standard when organizations have disciplined Scrum teams with full implementation of XP engineering practices inside the Scrum. Previous studies used overlapping time zones to ease communication and create a single distributed team. The goal of this report is to go one step further and show the same results with team members separated by the 12.5 hour time difference between India and San Francisco. If Scrum works without overlapping time zones then applying it to the mainstream offshoring practice in North America will be possible. In 2008, Xebia India started engagements with partners like TBD.com, a social networking site in San Francisco. TBD has an existing core team of developers doing Scrum with an established local velocity. Adding Xebia India developers to the San Francisco team with a Fully Distributed Scrum model achieved linear scalability with a globally distributed outsourced team.
Quantifying trust in autonomous system under uncertainties

Over the years, autonomous systems have entered almost all the facets of human life. Gradually, higher levels of autonomy are being incorporated into cyber-physical systems (CPS) and Internet-of-things (IoT) devices. However, safety and security has always been a lurking fear behind adoption of autonomous systems such as self-driving vehicles. To address these issues, we develop a framework for quantifying trust in autonomous system. This framework consist of an estimation method, which considers effect of adversarial attacks on sensor measurements. Our estimation algorithm uses a set-membership method during identification of safe states of the system. An important feature of this algorithm is that it can distinguish between adversarial noise and other disturbances. We also verify the autonomous system by first modeling it as networks of priced timed automata (NPTA) with stochastic semantics and then using statistical probabilistic model checking to verify it against probabilistic specifications. The verification process ensures that the autonomous system behave in accordance to safety specifications within a probabilistic threshold. For quantifying trust on the system, we use confidence results provided by the model checking tool. We have demonstrated our approach by using a case study of adaptive cruise control system under sensor spoofing attacks.
A Review of 25 Years of CSCW Research in Healthcare: Contributions, Challenges and Future Agendas

CSCW as a field has been concerned since its early days with healthcare, studying how healthcare work is collaboratively and practically achieved and designing systems to support that work. Reviewing literature from the CSCW Journal and related conferences where CSCW work is published, we reflect on the contributions that have emerged from this work. The analysis illustrates a rich range of concepts and findings towards understanding the work of healthcare but the work on the larger policy level is lacking. We argue that this presents a number of challenges for CSCW research moving forward: in having a greater impact on larger-scale health IT projects; broadening the scope of settings and perspectives that are studied; and reflecting on the relevance of the traditional methods in this field - namely workplace studies - to meet these challenges.
Hardware-Efficient Low-Power Image Processing System for Wireless Capsule Endoscopy

This paper presents the design of a hardware-efficient, low-power image processing system for next-generation wireless endoscopy. The presented system is composed of a custom CMOS image sensor, a dedicated image compressor, a forward error correction (FEC) encoder protecting radio transmitted data against random and burst errors, a radio data transmitter, and a controller supervising all operations of the system. The most significant part of the system is the image compressor. It is based on an integer version of a discrete cosine transform and a novel, low complexity yet efficient, entropy encoder making use of an adaptive Golomb-Rice algorithm instead of Huffman tables. The novel hardware-efficient architecture designed for the presented system enables on-the-fly compression of the acquired image. Instant compression, together with elimination of the necessity of retransmitting erroneously received data by their prior FEC encoding, significantly reduces the size of the required memory in comparison to previous systems. The presented system was prototyped in a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. Its power consumption is low and comparable to other application-specific-integrated-circuits-based systems, despite FPGA-based implementation.
Video (GIF) Sentiment Analysis using Large-Scale Mid-Level Ontology

With faster connection speed, Internet users are now making social network a huge reservoir of texts, images and video clips (GIF). Sentiment analysis for such online platform can be used to predict political elections, evaluates economic indicators and so on. However, GIF sentiment analysis is quite challenging, not only because it hinges on spatio-temporal visual contentabstraction, but also for the relationship between such abstraction and final sentiment remains unknown.In this paper, we dedicated to find outsuch relationship.We proposed a SentiPairSequence basedspatiotemporal visual sentiment ontology, which forms the midlevel representations for GIFsentiment. The establishment process of SentiPair contains two steps. First, we construct the Synset Forest to define the semantic tree structure of visual sentiment label elements. Then, through theSynset Forest, we organically select and combine sentiment label elements to form a mid-level visual sentiment representation. Our experiments indicate that SentiPair outperforms other competing mid-level attributes. Using SentiPair, our analysis frameworkcan achieve satisfying prediction accuracy (72.6%). We also opened ourdataset (GSO-2015) to the research community. GSO-2015 contains more than 6,000 manually annotated GIFs out of more than 40,000 candidates. Each is labeled with both sentiment and SentiPair Sequence.
Scalable Distributed Algorithm for Approximate Nearest Neighbor Search Problem in High Dimensional General Metric Spaces

We propose a novel approach for solving the approximate nearest neighbor search problem in arbitrary metric spaces. The distinctive feature of our approach is that we can incrementally build a non-hierarchical distributed structure for given metric space data with a logarithmic complexity scaling on the size of the structure and adjustable accuracy probabilistic nearest neighbor queries. The structure is based on a small world graph with vertices corresponding to the stored elements, edges for links between them and the greedy algorithm as base algorithm for searching. Both search and addition algorithms require only local information from the structure. The performed simulation for data in the Euclidian space shows that the structure built using the proposed algorithm has navigable small world properties with logarithmic search complexity at fixed accuracy and has weak (power law) scalability with the dimensionality of the stored data.