snu-aidas/HVMap-dataset huggingface dataset:
ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple
sclerosis lesion heterogeneity, as demonstrated by quantitative MRI

BACKGROUND
There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans.


OBJECTIVE
To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images.


METHODS
We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML.


RESULTS
Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively.


CONCLUSION
ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.
Rethinking the Value of Network Pruning

Network pruning is widely used for reducing the heavy computational cost of 1 deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a 2 large model), pruning and fine-tuning. In this work, we make a rather surprising 3 observation: fine-tuning a pruned model only gives comparable or even worse 4 performance than training that model with randomly initialized weights. Our 5 results have several implications: 1) training a large, over-parameterized model is 6 not necessary to obtain an efficient final model, 2) learned “important” weights 7 of the large model are not necessarily useful for the small pruned model, 3) the 8 pruned architecture itself, rather than a set of inherited weights, is what leads to the 9 efficiency benefit in the final model, which suggests that some pruning algorithms 10 could be seen as performing network architecture search. 11
Hybrid Recommender Systems: Survey and Experiments

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
Method to find community structures based on information centrality.

Community structures are an important feature of many social, biological, and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries [M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)]. We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality. We test the algorithm on computer generated and real-world networks whose community structure is already known or has been studied by means of other methods. We show that our algorithm, although it runs to completion in a time O(n4) , is very effective especially when the communities are very mixed and hardly detectable by the other methods.
Collaborative Recommendation via Adaptive Association Rule Mining

Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among users' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are ine cient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm speci cally designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be speci ed in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a speci c target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is signi cantly better than that of traditional correlationbased approaches. Corresponding author. Present a liation: Department of Computer Science, Wellesley College, 106 Central Street, Wellesley, MA 02481 USA, e-mail: salvarez@wellesley.edu
Bullying, cyberbullying, and mental health in young people.

OBJECTIVE
To investigate the factors associated with exposure to in-real-life (IRL) bullying, cyberbullying, and both IRL and cyberbullying and to explore the relationship between these types of bullying and mental health among 13-16-year-old Swedish boys and girls.


METHODS
Data was derived from a cross-sectional web-based study of 13-16-year-old students in northern Sweden (n=1214, response rate 81.9%).


RESULTS
The combination of IRL- and cyberbullying was the most common type of bullying. A non-supportive school environment and poor body image were related to exposure to bullying for both genders but the relationship was more distinct in girls. All types of bullying were associated with depressive symptoms in both boys and girls and all forms of bullying increased the likelihood of psychosomatic problems in girls.


CONCLUSIONS
Cyberbullying can be seen as an extension of IRL bullying. A combination of IRL- and cyberbullying seems to be particularly negative for mental health. Interventions should focus on improved school environment and body image as well as anti-violence programmes. Gender aspects of bullying need to be acknowledged.
Matrix computations (3. ed.)
Wheat glutenin subunits and dough elasticity: findings of the EUROWHEAT project

*IACR-Long Ashton Research Station, Department of Agricultural Sciences, University of Bristol, Long Ashton BS41 9AF, UK (tel: +1275-392181; fax: +1275-394299; e-mail: peter.shewry@bbsrc.ac.uk) {Institut National de la Recherche Agronomique, Centre de Recherches de Nantes, Laboratoire de BiochimieetdeTechnologiedesProtéines,B.P.71627, Ruede laGéraudière,Nantes 44316,Cedex03, France xDipartimento di Agrobiologia ed Agrochimica, Via San Camillo de Lellis, Viterbo 01100, Lazio, Italy {Institute of Food Research, Norwich Laboratory, Norwich Research Park, Colney Lane, Norwich NR4 7UA, UK
Automated extraction of product comparison matrices from informal product descriptions

Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users. Preprint submitted to JSS January 5, 2017
Gender identity and sexual orientation in women with borderline personality disorder.

INTRODUCTION
In the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, text revision (DSM-IV-TR) (and earlier editions), a disturbance in "identity" is one of the defining features of borderline personality disorder (BPD). Gender identity, a person's sense of self as a male or a female, constitutes an important aspect of identity formation, but this construct has rarely been examined in patients with BPD.


AIMS
In the present study, the presence of gender identity disorder or confusion was examined in women diagnosed with BPD.


MAIN OUTCOME MEASURES
We used a validated dimensional measure of gender dysphoria. Recalled gender identity and gender role behavior from childhood was also assessed with a validated dimensional measure, and current sexual orientation was assessed by two self-report measures.


METHODS
A consecutive series of 100 clinic-referred women (mean age, 34 years) with BPD participated in the study. The women were diagnosed with BPD using the International Personality Disorder Exam-BPD Section.


RESULTS
None of the women with BPD met the criterion for caseness on the dimensional measure of gender dysphoria. Women who self-reported either a bisexual or a homosexual sexual orientation had a significantly higher score on the dimensional measure of gender dysphoria than the women who self-reported a heterosexual sexual orientation, and they also recalled significantly more cross-gender behavior during childhood. Results were compared with a previous study on a diagnostically heterogeneous group of women with other clinical problems.


CONCLUSION
The importance of psychosexual assessment in the clinical evaluation of patients with BPD is discussed.
Constraints in the IoT: The World in 2020 and Beyond

The Internet of Things (IoT), often referred as the future Internet; is a collection of interconnected devices integrated into the world-wide network that covers almost everything and could be available anywhere. IoT is an emerging technology and aims to play an important role in saving money, conserving energy, eliminating gap and better monitoring for intensive management on a routine basis. On the other hand, it is also facing certain design constraints such as technical challenges, social challenges, compromising privacy and performance tradeoffs. This paper surveys major technical limitations that are hindering the successful deployment of the IoT such as standardization, interoperability, networking issues, addressing and sensing issues, power and storage restrictions, privacy and security, etc. This paper categorizes the existing research on the technical constraints that have been published in the recent years. With this categorization, we aim to provide an easy and concise view of the technical aspects of the IoT. Furthermore, we forecast the changes influenced by the IoT. This paper predicts the future and provides an estimation of the world in year 2020 and beyond. Keywords—Internet of Things; Future Internet; Next generation network issues; World-wide network; 2020
Traffic sign detection and recognition based on random forests

In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI raffic sign detection raffic sign recognition olor segmentation andom forests upport vector machines (SVMs) istogram of oriented gradients (HOG) color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods. © 2016 Elsevier B.V. All rights reserved. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 ocal self-similarity (LSS)
Haraka - Efficient Short-Input Hashing for Post-Quantum Applications

Recently, many efficient cryptographic hash function design strategies have been explored, not least because of the SHA-3 competition. These designs are, almost exclusively, geared towards high performance on long inputs. However, various applications exist where the performance on short (fixed length) inputs matters more. Such hash functions are the bottleneck in hash-based signature schemes like SPHINCS or XMSS, which is currently under standardization. Secure functions specifically designed for such applications are scarce. We attend to this gap by proposing two short-input hash functions (or rather simply compression functions). By utilizing AES instructions on modern CPUs, our proposals are the fastest on such platforms, reaching throughputs below one cycle per hashed byte even for short inputs, while still having a very low latency of less than 60 cycles. Under the hood, this results comes with several innovations. First, we study whether the number of rounds for our hash functions can be reduced, if only second-preimage resistance (and not collision resistance) is required. The conclusion is: only a little. Second, since their inception, AES-like designs allow for supportive security arguments by means of counting and bounding the number of active S-boxes. However, this ignores powerful attack vectors using truncated differentials, including the powerful rebound attacks. We develop a general tool-based method to include arguments against attack vectors using truncated differentials.
Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set

ISSN 2250 – 1061 | © 2011 Bonfring Abstract--In machine learning, classification refers to a step by step procedure for designating a given piece of input data into any one of the given categories. There are many classification problem occurs and need to be solved. Different types are classification algorithms like tree-based, rule-based, etc are widely used. This work studies the effectiveness of Rule-Based classifiers for classification by taking a sample data set from UCI machine learning repository using the open source machine learning tool. A comparison of different rulebased classifiers used in Data Mining and a practical guideline for selecting the most suited algorithm for a classification is presented and some empirical criteria for describing and evaluating the classifiers are given.
INTEGRATING MOOCs IN BLENDED COURSES

Recent studies appreciate that "MOOCs bring an impetus of reform, research and innovation to the Academy" [9]. Even though MOOCs are usually developed and delivered as independent online courses, experiments to wrap formal university courses around existing MOOCs are reported by teachers and researchers in different articles [3], [4], [8], [17]. This paper describes a new approach, in which the participation of students in different MOOCs was integrated in a blended course run on a social mobile LMS. The topics of MOOCs delivered on specific platforms and having particular characteristics were connected with the Fall 2013 undergraduate course of Web Programming, at University Politehnica Timisoara, Romania, facilitated by the first author, the co-authors providing a shadow/peer facilitation, for tailoring the course scenario. The main parts of this study deal with: a) The reasons to integrate MOOCs in the university course. b) How the course was designed, how the students’ activities on different MOOC platforms were assessed and integrated in the course scenario. c) The results of a survey that evaluates students’ experiences related to MOOCs: a number of MOOC features were assessed [14]; answers to a few problems are also analysed: Did the participation in MOOCs support students to clarify and expand the course issues? What are students' suggestions for a more active participation in MOOCs? By comparing the learning scenarios of MOOCs with the Web Programming blended course, how can the course and its virtual space be improved? Do students consider that the MOOCs phenomenon is important for professional and personal development? The conclusions of the paper can be used by other teachers/instructors for integrating MOOCs in the courses they deliver/facilitate.
Modeling social influence through network autocorrelation: constructing the weight matrix

Many physical and social phenomena are embedded within networks of interdependencies, the so-called ‘context’ of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models, hinge upon the chosen specification of weight matrix W, the elements of which represent the influence pattern present in the network. In this paper I discuss how social influence processes can be incorporated in the specification of W. Theories of social influence center around ‘communication’ and ‘comparison’; it is discussed how these can be operationalized in a network analysis context. Starting from that, a series of operationalizations of W is discussed. Finally, statistical tests are presented that allow an analyst to test various specifications against one another or pick the best fitting model from a set of models. © 2002 Elsevier Science B.V. All rights reserved.
Effect of gas type and flow rate on Cu free air ball formation in thermosonic wire bonding

0026-2714/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.microrel.2010.02.023 * Corresponding author. E-mail address: apequegn@uwaterloo.ca (A. Peque The development of novel Cu wires for thermosonic wire bonding is time consuming and the effects of shielding gas on the electrical flame off (EFO) process is not fully understood. An online method is used in this study for characterizing Cu free air balls (FABs) formed with different shielding gas types and flow rates. The ball heights before (HFAB) and after deformation (Hdef) are responses of the online method and measured as functions of gas flow rate. Sudden changes in the slopes of these functions, a non-parallelity of the two functions, and a large standard deviation of the HFAB measurements all identify FAB defects. Using scanning electron microscope (SEM) images in parallel with the online measurements golf-club shaped and pointed shaped FABs are found and the conditions at which they occur are identified. In general FAB defects are thought to be caused by changes in surface tension of the molten metal during EFO due to inhomogeneous cooling or oxidation. It is found that the convective cooling effect of the shielding gas increases with flow rate up to 0.65 l/min where the bulk temperature of a thermocouple at the EFO site decreases by 19 C. Flow rates above 0.7 l/min yield an undesirable EFO process due to an increase in oxidation which can be explained by a change in flow from laminar to turbulent. The addition of H2 to the shielding gas reduces the oxidation of the FAB as well as providing additional thermal energy during EFO. Different Cu wire materials yield different results where some perform better than others. 2010 Elsevier Ltd. All rights reserved.
A whole-body control framework for humanoids operating in human environments

Tomorrow's humanoids will operate in human environments, where efficient manipulation and locomotion skills, and safe contact interactions are critical design factors. We report here our recent efforts into these issues, materialized into a whole-body control framework. This framework integrates task-oriented dynamic control and control prioritization allowing to control multiple task primitives while complying with physical and movement-related constraints. Prioritization establishes a hierarchy between control spaces, assigning top priority to constraint-handling tasks, while projecting operational tasks in the null space of the constraints, and controlling the posture within the residual redundancy. This hierarchy is directly integrated at the kinematic level, allowing the program to monitor behavior feasibility at runtime. In addition, prioritization allows us to characterize the dynamic behavior of the individual control primitives subject to the constraints, and to synthesize operational space controllers at multiple levels. To complete this framework, we have developed free-floating models of the humanoid and incorporate the associated dynamics and the effects of the resulting support contacts into the control hierarchy. As part of a long term collaboration with Honda, we are currently implementing this framework into the humanoid robot Asimo