snu-aidas/HVMap-dataset huggingface dataset:
Hardware-software integrated approaches to defend against software cache-based side channel attacks

Software cache-based side channel attacks present serious threats to modern computer systems. Using caches as a side channel, these attacks are able to derive secret keys used in cryptographic operations through legitimate activities. Among existing countermeasures, software solutions are typically application specific and incur substantial performance overhead. Recent hardware proposals including the Partition-Locked cache (PLcache) and Random-Permutation cache (RPcache) [23], although very effective in reducing performance overhead while enhancing the security level, may still be vulnerable to advanced cache attacks. In this paper, we propose three hardware-software approaches to defend against software cache-based attacks - they present different tradeoffs between hardware complexity and performance overhead. First, we propose to use preloading to secure the PLcache. Second, we leverage informing loads, which is a lightweight architectural support originally proposed to improve memory performance, to protect the RPcache. Third, we propose novel software permutation to replace the random permutation hardware in the RPcache. This way, regular caches can be protected with hardware support for informing loads. In our experiments, we analyze various processor models for their vulnerability to cache attacks and demonstrate that even to the processor model that is most vulnerable to cache attacks, our proposed software-hardware integrated schemes provide strong security protection.
An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS).

OBJECTIVES
To demonstrate the use of Rasch analysis by assessing the appropriateness of utilizing the Hospital Anxiety and Depression Scale (HADS) total score (HADS-14) as a measure of psychological distress.


DESIGN
Cross-sectional, using Rasch analysis.


METHODS
The HADS was administered to 296 patients attending an out-patient musculoskeletal rehabilitation program. Rasch analysis was conducted using RUMM2020 software to assess the overall fit of the model, the response scale used, individual item fit, differential item functioning (DIF) and person separation.


RESULTS
Rasch analysis supported the viability of the HADS-14 as a measure of psychological distress. It showed good person separation, little disordering of the thresholds and no evidence of DIE One anxiety item (item 11) showed some misfit to the model. The residuals patterned into the two subscales (anxiety and depression), but the person estimate derived from these two subscales was not statistically different to that derived from all items taken together, supporting the assumption of unidimensionality. A cut-point of 12 on the HADS-14 identified all cases that were classified as both anxious and depressed on the original individual HADS subscales.


CONCLUSIONS
The results of Rasch analysis support the use of the HADS-14 as a global measure of psychological distress. The study demonstrates the usefulness of Rasch analysis in assessing the psychometric properties of a scale and suggests that further use of this technique to assess the HADS-14 in other clinical groups is warranted.
High Resolution AMOLED Pixel Using Negative Feedback Structure for Improving Image Quality

Abstract An active matrix organic light emitting diode (AMOLED) pixel structure is proposed to improve image quality of high resolution small sized displays. The proposed pixel structure increases luminance uniformity by decreasing the distortion of the voltage in the storage capacitors using negative feedback structure. The simulation results show that the distortion of the voltage in the storage capacitors with negative feedback structure decreases to 55.3% compared to that without negative feedback structure and the emission current error of the proposed pixel structure ranges from –0.71 LSB to 0.56 LSB when the threshold voltage of the driving TFT varies from –0.2 V to 0.2 V.
Integrating shape grammars into a generative system for Zhuang ethnic embroidery design exploration

The integration of shape grammars within a generative system is thought to have good potential to support design applications in the art and cultural domains. Based on a shape decomposing method, two-level generative shape grammars are developed within the application of Zhuang ethnic embroidery design. In our approach, free B-spline curves are used to derive initial design concepts together with a new interpolation algorithm. In order to cope with the potentially huge solution space, a novel aesthetic evaluation model and a design exploration algorithm are developed for designers to find their favorite design solutions based on their subjective assessments. A system is introduced to demonstrate this research with an example of Zhuang ethnic embroidery design. © 2012 Elsevier Ltd. All rights reserved.
How robust are prediction effects in language comprehension? Failure to replicate article-elicited N400 effects

Current psycholinguistic theory proffers prediction as a central, explanatory mechanism in language processing. However, widely-replicated prediction effects may not mean that prediction is necessary in language processing. As a case in point, C. D. Martin et al. [2013. Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language, 69(4), 574–588. doi:10.1016/j.jml.2013.08.001] reported ERP evidence for prediction in nativebut not in non-native speakers. Articles mismatching an expected noun elicited larger negativity in the N400 time window compared to articles matching the expected noun in native speakers only. We attempted to replicate these findings, but found no evidence for prediction irrespective of language nativeness. We argue that pre-activation of phonological form of upcoming nouns, as evidenced in article-elicited effects, may not be a robust phenomenon. A view of prediction as a necessary computation in language comprehension must be re-evaluated. ARTICLE HISTORY Received 1 June 2016 Accepted 22 September 2016
Katyusha: the first direct acceleration of stochastic gradient methods

Nesterovâs momentum trick is famously known for accelerating gradient descent, and has been proven useful in building fast iterative algorithms. However, in the stochastic setting, counterexamples exist and prevent Nesterovâs momentum from providing similar acceleration, even if the underlying problem is convex. We introduce Katyusha, a direct, primal-only stochastic gradient method to fix this issue. It has a provably accelerated convergence rate in convex (off-line) stochastic optimization. The main ingredient is Katyusha momentum, a novel ânegative momentumâ on top of Nesterovâs momentum that can be incorporated into a variance-reduction based algorithm and speed it up. Since variance reduction has been successfully applied to a growing list of practical problems, our paper suggests that in each of such cases, one could potentially give Katyusha a hug.
A maximum entropy approach to adaptive statistical language modelling

An adaptive statistical language model is described, which successfully integrates long distance linguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document’s history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. The ME framework is extremely general: any phenomenon that can be described in terms of statistics of the text can be readily incorporated. An adaptive language model based on the ME approach was trained on the Wall Street Journal corpus, and showed a 32–39% perplexity reduction over the baseline. When interfaced to SPHINX-II, Carnegie Mellon’s speech recognizer, it reduced its error rate by 10–14%. This thus illustrates the feasibility of incorporating many diverse knowledge sources in a single, unified statistical framework.  1996 Academic Press Limited
Long-Term Treatment with High-Dose Thiamine in Parkinson Disease: An Open-Label Pilot Study.

OBJECTIVES
To investigate the potential clinical, restorative, and neuroprotective effects of long-term treatment with thiamine in Parkinson disease (PD).


DESIGN
Observational open-label pilot study.


SETTING
Outpatient neurologic rehabilitation clinic.


PATIENTS AND METHODS
Starting in June 2012, we have recruited 50 patients with PD (33 men and 17 women; mean age, 70.4 ± 12.9 years; mean disease duration, 7.3 ± 6.7 years). All the patients were assessed at baseline with the Unified Parkinson's Disease Rating Scale (UPDRS) and the Fatigue Severity Scale (FSS) and began treatment with 100 mg of thiamine administered intramuscularly twice a week, without any change to personal therapy. All the patients were re-evaluated after 1 month and then every 3 months during treatment.


RESULTS
Thiamine treatment led to significant improvement of motor and nonmotor symptoms: mean UPDRS scores (parts I-IV) improved from 38.55 ± 15.24 to 18.16 ± 15.08 (p = 2.4 × 10(-14), t test for paired data) within 3 months and remained stable over time; motor UPDRS part III score improved from 22.01 ± 8.57 to 9.92 ± 8.66 (p = 3.1 × 10(-22)). Some patients with a milder phenotype had complete clinical recovery. FSS scores, in six patients who had fatigue, improved from 53.00 ± 8.17 to 23.60 ± 7.77 (p < 0.0001, t test for paired data). Follow-up duration ranged from 95 to 831 days (mean, 291.6 ± 207.2 days).


CONCLUSIONS
Administration of parenteral high-dose thiamine was effective in reversing PD motor and nonmotor symptoms. The clinical improvement was stable over time in all the patients. From our clinical evidence, we hypothesize that a dysfunction of thiamine-dependent metabolic processes could cause selective neural damage in the centers typically affected by this disease and might be a fundamental molecular event provoking neurodegeneration. Thiamine could have both restorative and neuroprotective action in PD.
Recommendations for designers and researchers resulting from the world-wide failure exercise

The World-Wide Failure Exercise (WWFE) contained a detailed assessment of 19 theoretical approaches for predicting the deformation and failure response of polymer composite laminates when subjected to complex states of stress. The leading five theories are explored in greater detail to demonstrate their strengths and weaknesses in predicting various types of structural failure. Recommendations are then derived, as to how the theories can be best utilised to provide safe and economic predictions in a wide range of engineering design applications. Further guidance is provided for designers on the level of confidence and bounds of applicability of the current theories. The need for careful interpretation of initial failure predictions is emphasised, as is the need to allow for multiple sources of non-linearity (including progressive damage) where accuracy is sought for certain classes of large deformation and final failure strength predictions. Aspects requiring further experimental and theoretical investigation are identified. Direction is also provided to the research community by highlighting specific, tightly focussed, experimental and theoretical studies that, if carried out in the very near future, would pay great dividends from the designer’s perspective, by increasing their confidence in the theoretical foundations. # 2003 QinetiQ Ltd. Published by Elsevier Ltd. All rights reserved.
Automated Fingerprint Identification and Imaging Systems

More than a century has passed since Alphonse Bertillon first conceived and then industriously practiced the idea of using body measurements for solving crimes [1]. Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and pract ic l discovery of the uniqueness of the human fingerprints 1. Soon after this discovery, many major law enforcement departments embraced the idea of first “booking” the fingerprints of criminals, so tha t their records are readily available and later using leftover fingerprint smudges (latent s), the identity of criminals can be determined. These agencies sponsored a rigorous study of fingerprints, developed s cientific 1In 1893, the Home Ministry Office, UK, accepted that no two ind ividuals have the same fingerprints.
Automatic Linguistic Annotation of Large Scale L 2 Databases : The EF-Cambridge Open Language Database ( EFCamDat )

∗Naturalistic learner productions are an important empirical resource for SLA research. Some pioneering works have produced valuable second language (L2) resources supporting SLA research.1 One common limitation of these resources is the absence of individual longitudinal data for numerous speakers with different backgrounds across the proficiency spectrum, which is vital for understanding the nature of individual variation in longitudinal development.2 A second limitation is the relatively restricted amounts of data annotated with linguistic information (e.g., lexical, morphosyntactic, semantic features, etc.) to support investigation of SLA hypotheses and obtain patterns of development for different linguistic phenomena. Where available, annotations tend to be manually obtained, a situation posing immediate limitations to the quantity of data that could be annotated with reasonable human resources and within reasonable time. Natural Language Processing (NLP) tools can provide automatic annotations for parts-of-speech (POS) and syntactic structure and are indeed increasingly applied to learner language in various contexts. Systems in computer-assisted language learning (CALL) have used a parser and other NLP tools to automatically detect learner errors and provide feedback accordingly.3 Some work aimed at adapting annotations provided by parsing tools to accurately describe learner syntax (Dickinson & Lee, 2009) or evaluated parser performance on learner language and the effect of learner errors on the parser. Krivanek and Meurers (2011) compared two parsing methods, one using a hand-crafted lexicon and one trained on a corpus. They found that the former is more successful in recovering the main grammatical dependency relations whereas the latter is more successful in recovering optional, adjunction relations. Ott and Ziai (2010) evaluated the performance of a dependency parser trained on native German (MaltParser; Nivre et al., 2007) on 106 learner answers to a comprehension task in L2 German. Their study indicates that while some errors can be problematic for the parser (e.g., omission of finite verbs) many others (e.g., wrong word order) can be parsed robustly, resulting in overall high performance scores. In this paper we have two goals. First, we introduce a new English L2 database, the EF Cambridge Open Language Database, henceforth EFCAMDAT. EFCAMDAT was developed by the Department of Theoretical and Applied Linguistics at the University of Cambridge in collaboration with EF Education First, an international educational organization. It contains writings submitted to Englishtown, the
An Approach to Data Privacy in Smart Home using Blockchain Technology

Nowadays, numerous applications of smart home systems provide recommendations for users, including reducing their energy consumption, warnings of defective devices, selecting reliable devices and software, diagnoses, etc [1]. The internet connected, dynamic and heterogeneous nature of the smart home environment creates new security, authentication, and privacy challenges [2]. To solve those challenges, an approach to data privacy in smart home using blockchain technology, which is called smart home based the IoT-Blockchain (SHIB), is proposed in this paper. In order to demonstrate the proposed architecture, an experimental scenario using Ganache, Remix, and web3. js is built among the user, service provider, and smart home to evaluate the performance of the smart contract in the SHIB. Based on the experiment results, the SHIB architecture brings the advantages like data privacy, trust access control, and high extension ability. In addition, the comparison between the proposed architecture and existing models in different parameters such as smart contract, the privacy of data, usage of tokens, updating the policies, and misbehavior judging are performed.
Systematic literature reviews in software engineering - A systematic literature review

Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners. ! 2008 Elsevier B.V. All rights reserved.
Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

BACKGROUND
The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification.


METHODS
We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors.


RESULTS
The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist.


CONCLUSION
We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.
Predictors of loneliness among older women and men in Sweden: A national longitudinal study.

OBJECTIVES
Longitudinal research on loneliness in old age has rarely considered loneliness separately for men and women, despite gender differences in life experiences. The objective of this study was to examine the extent to which older women and men (70+) report feelings of loneliness with a focus on: (a) changes in reported loneliness as people age, and (b) which factors predict loneliness.


METHOD
Data from the 2004 and 2011 waves of SWEOLD, a longitudinal national survey, was used (n = 587). The prediction of loneliness in 2011 by variables measured in 2004 and 2004-2011 variable change scores was examined in three logistic regression models: total sample, women and men. Variables in the models included: gender, age, education, mobility problems, depression, widowhood and social contacts.


RESULTS
Older people moved into and out of frequent loneliness over time, although there was a general increase in loneliness with age. Loneliness at baseline, depression increment and recent widowhood were significant predictors of loneliness in all three multivariable models. Widowhood, depression, mobility problems and mobility reduction predicted loneliness uniquely in the model for women; while low level of social contacts and social contact reduction predicted loneliness uniquely in the model for men.


CONCLUSION
This study challenges the notion that feelings of loneliness in old age are stable. It also identifies important gender differences in prevalence and predictors of loneliness. Knowledge about such differences is crucial for the development of effective policy and interventions to combat loneliness in later life.
Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it’s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. * Corresponding author