nils-herrmann/clean_scirepeval huggingface dataset:
Interactive Nonlinear Multiobjective Optimization Methods

An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the her or his most satisfactory Pareto optimal solution can be found. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the role of the decision maker is very important in interactive methods, methods presented are classified according to the type of preference information that the decision maker is assumed to provide.
Discriminant component eigenfaces for privacy preserving face recognition
Venue: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

Over the past decades, face recognition has been a problem of critical interest in the machine learning and signal processing communities. However, conventional approaches such as eigenfaces do not protect the privacy of user data, which is emerging as an important design consideration in today's society. In this work, we leverage a supervised learning subspace projection method called Discriminant Component Analysis (DCA) for privacy preserving face recognition. By projecting the data onto the lower dimensional signal subspace prescribed by DCA, high performance of face recognition is achievable without compromising privacy of the data owners. We evaluate our approach on three image datasets: Yale, Olivetti and Glasses datasets the last is derived from the former two. Our approach can serve as a key enabler for real world deployment of privacy preserving face recognition applications, and provides a promising direction to researchers and private sectors.
Privacy preserving face recognition with outsourced computation
Venue: Soft Comput.

Face recognition is one of the most important biometrics pattern recognitions, which has been widely applied in a variety of enterprise, civilian and law enforcement. The privacy of biometrics data raises important concerns, in particular if computations over biometric data is performed at untrusted servers. In previous work of privacy preserving face recognition, in order to protect individuals' privacy, face recognition is performed over encrypted face images. However, these results increase the computation cost of the client and the face database owners, which may enable face recognition not to be executed. Consequently, it would be desirable to reduce computation cost over sensitive biometric data in such environments. Currently, no secure techniques for outsourcing face biometric recognition are readily available. In this paper, we propose a privacy preserving face recognition protocol with outsourced computation for the first time, which efficiently protects individuals' privacy. Our protocol substantially improves the previous works in terms of the online computation cost by outsourcing large computation task to a cloud server who has large computing power. In particular, the overall online computation cost of the client and the database owner in our protocol is at most 1/2 of the corresponding protocol in the state of the art algorithms. In addition, the client requires the decryption operations with only O(1) independent of M, where M is the size of the face database. Furthermore, the client can verify the correctness of the recognition result.
Psychometric properties of the Posttraumatic Diagnostic Scale for DSM 5 (PDS 5)
Venue: Psychological assessment

An Erratum for this article was reported in Vol 28(10) of Psychological Assessment (see record 2016 36101 001) In the article, the third sentence of the Internal Consistency subsection of the Results section should read: Item 8, "Not being able to remember important parts of the trauma," had an item total correlation of .34, and Item 16, "Taking more risks or doing things that might cause you or others harm" had an item total correlation of .44; the range of item total correlations for the remaining 18 items was .62 .82, with an average of .70." The aim of this study was to evaluate the psychometric properties of the Posttraumatic Diagnostic Scale for DSM 5 (PDS 5) a self report measure of posttraumatic stress disorder (PTSD) based on diagnostic criteria of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. Participants were 242 urban community residents, veterans, and college undergraduates recruited from 3 study sites who had experienced a DSM 5 Criterion A traumatic experience. The PDS 5 demonstrated excellent internal consistency (a .95) and test retest reliability (r .90) and good convergent validity with the PTSD Checklist Specific Version (r .90) and the PTSD Symptom Scale Interview Version for DSM 5 (PSSI 5; r .85) The PDS 5 also showed good discriminant validity with the Beck Depression Inventory II and the State Trait Anxiety Inventory Trait scale (all ZH 3.05, ps .01) There was 78% agreement between the PDS 5 and the PSSI 5. Receiver operating characteristic analysis yielded a cutoff score of 28 for identifying a probable PTSD diagnosis. The PDS 5 is a valid and reliable measure of DSM 5 PTSD symptomatology.
A Time Aware Method for Predicting Dull Nodes and Links in Evolving Networks for Data Cleaning
Venue: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

Existing studies on evolution of social network largely focus on addition of new nodes and links in the network. However, as network evolves, existing relationships degrade and break down, and some nodes go to hibernation or decide not to participate in any kind of activities in the network where it belongs. Such nodes and links, which we refer as "dull" may affect analysis and prediction tasks in networks. This paper formally defines the problem of predicting dull nodes and links at an early stage, and proposes a novel time aware method to solve it. Pruning of such nodes and links is framed as "network data cleaning" task. As the definitions of dull node and link are non trivial and subjective, a novel scheme to label such nodes and links is also proposed here. Experimental results on two real network datasets demonstrate that the proposed method accurately predicts potential dull nodes and links. This paper further experimentally validates the need for data cleaning by investigating its effect on the well known "link prediction" problem.
A supervised approach for intra /inter community interaction prediction in dynamic social networks
Venue: Social Network Analysis and Mining

Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e. users in online social networks, researchers in collaboration networks) In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra community and inter community link prediction. Experimental results on real time stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.
Effects of speaker s and listener s acoustic environments on speech intelligibility and annoyance

Speech signal modification to increase intelligibility in noisy environments may cause discomfort in quieter environments. Human speech production in noise is known to improve intelligibility in noise. The socalled Lombard effect might cause annoyance in quiet environments. However, the effect disappears in quiet environments. This suggests that humans might modify their voices depending on the acoustic environment considering intelligibility and discomfort. The speech modification algorithms which use rules obtained from human speech production are expected to meet the demand for both of high intelligibility and reducing discomfort in various acoustic environments. This paper examined the influence of the coincidence between speaker's and listener's acoustic environments on speech intelligibility and the level of annoyance and to confirm the expectation. Speech produced by four Japanese speakers in quiet and noisy environments (at non, 66, 78, 90 dB) were presented to Japanese listeners in quiet and noisy environments (at non, 66, 78 90 dB) to measure intelligibility of words and the level of annoyance. The results showed that speech is less intelligible or more annoying when the listener's environment is different from the speaker's one. This supports that mimicking human speech production would meet the demand for high intelligibility and less annoyance.
Adsorption mechanism on metal organic frameworks of Cu BTC, Fe BTC and ZIF 8 for CO2 capture investigated by X ray absorption fine structure

Three different commercial metal organic frameworks (MOFs) of Cu BTC, Fe BTC and ZIF 8 have been characterized by multiple techniques including CO2 adsorption/desorption, scanning electron microscopy (SEM) transmission electron microscopy (TEM) X ray diffraction (XRD) and X ray absorption fine structure (XAFS) Particularly, the MOFs powders were sealed inside quartz sample tubes for each step of CO2 adsorption/desorption, and were sequentially measured for XAFS to identify the short range structural evolutions on investigated metals (Cu, Fe, Zn) According to the data analyses on X ray absorption near edge spectroscope (XANES) and extended X ray absorption fine structure (EXAFS) we can demonstrate that both electronic and local structures of MOFs were almost unchanged between fresh samples and those under the activated/adsorbed/desorbed conditions. This indicates that the CO2 adsorption of MOFs is mainly governed by the physical driving force. On the other hand, the chemical transformation, i.e. elimination of water in MOFs structure by the activation (13.3 kPa, 473 K, 12 h) was found for Cu BTC only.
Iterative design of emetine based prodrug targeting fibroblast activation protein (FAP) and dipeptidyl peptidase IV DPPIV using a tandem enzymatic activation strategy
Venue: The Prostate

There is an urgent need to develop new agents for treating metastatic prostate cancer to overcome multiple drug resistance to the current standard targeted cancer therapy. Emetine is a highly cytotoxic natural product protein synthesis inhibitor, which is toxic to all cell types. Its cytotoxicity can be blocked by derivatizing its N 2' position. Thus emetine can be selectively delivered to cancer cells in the region of metastatic cancer as a prodrug that will be activated by an enzyme selectively overexpressed within the metastatic tumor microenvironment. In this work, we convert emetine to a prodrug activatable by the fibroblast activation protein (FAP) a serine protease overexpressed by the carcinoma associated fibroblasts.
Stromal factors involved in human prostate cancer development, progression and castration resistance
Venue: Journal of Cancer Research and Clinical Oncology

PurposeTo detect new predictive markers from the prostate cancer tissue, to study the expression by cultured cancer associated fibroblasts (CAFs) of stromal factors implicated in prostate carcinogenesis, and to compare their expressions in localized, metastatic, castration sensitive (CSCP) castration resistant prostate tumors (CRCP) as well as in fibroblasts from benign prostatic hyperplasia (BPH).Materials and methodsThe genomic expression of 20 stroma derived factors, including the androgen receptor (AR) growth factors (FGF2, FGF7, FGF10, HGF, TGFb, PDGFB) protein implicated in invasion (MMP 2, MMP 9 and MMP 11) inflammation (IL 6, IL 17, STAT 3 and NFkB) stroma/epithelium interaction (CDH11, FAP, CXCL12 and CXCL14) and chaperones (HPA1A and HSF1) was evaluated in cultured fibroblasts both from BHP and prostate carcinomas (PCa) After isolation and culture of fibroblasts by biopsy specimens, RNA was isolated and genomic studies performed.ResultsFinally, 5 BPH and 37 PCa specimens were selected: clinically localized (19) metastatic (5) CSCP (7) and CRPC (6) Interleukin 17 receptor (IL 17RB) was highly expressed in CAFs compared with fibroblasts from BPH. However, metalloproteinase 2 and chemokine ligand 14 (CXCL14) were expressed at higher levels by fibroblasts from BPH. The fibroblastic growth factor 7 was highly expressed by CAFs from localized tumors, but metalloproteinase 11 in metastatic tumors. MMP 11, androgen receptor (AR) and heat shock 70kda protein 1A (HSPA1A) expressions were significantly higher in CAFs from CRPC.ConclusionsThese results demonstrate a CAFs heterogeneity among prostate carcinomas with regard to some molecular profile expressions that may be relevant in tumor development (IL 17RB) progression (MMP 11) and castration resistance (AR, MMP 11 and HSPA1A)
Impacts of urbanization on the prevalence of antibiotic resistant Escherichia coli in the Chaophraya River and its tributaries.
Venue: Water science and technology a journal of the International Association on Water Pollution Research

River water samples were taken from 32 locations around the basin of Chaophraya River and its four major tributaries in Thailand to investigate resistance ratios of Escherichia coli isolates to eight antibiotic agents of amoxicillin, sulfamethoxazole/trimethoprim, tetracycline, doxytetracycline, ciprofloxacin, levofloxacin, norfloxacin and ofloxacin. Principal component analysis was performed to characterize resistance patterns of the samples. Relevancy of the obtained principal components with urban land use and fecal contamination of the river were examined. The ratio of antibiotic resistant bacteria is likely to increase when urban land use near the sampling site exceeds a certain ratio. The resistance ratio to fluoroquinolones tends to be high in a highly populated area. Meanwhile, no significant contribution of fecal contamination was found to increase the resistance ratio. These results suggest that an antibiotic resistance ratio is dependent on conditions of local urbanization rather than the upstream conditions, and that the major sources of antibiotic resistant bacteria in the Chaophraya River basin are possibly point sources located in the urban area which contains a high ratio of resistant bacteria.
Institutional Investors as Information Producers around Open Market Share Repurchase Programs

We use transaction level institutional trading data and the data on US firms' actual repurchases made available by the 2003 revisions to the Exchange Act to analyze the ability of institutional investors to create an information advantage for themselves around open market share repurchases. We document several results new to the literature. First, institutional trading prior to an open market share repurchase (OMR) announcement has significant predictive power for the magnitude of the abnormal return to the repurchasing firm's equity around OMR announcements. Second, institutional trading immediately after an OMR announcement has significant predictive power for the firm's subsequent stock return performance: the larger the net buying by institutions, better the subsequent long run stock returns. Third, institutional trading immediately after an OMR announcement has significant predictive power for the actual repurchases by the firm: a larger amount of net buying of the firm's equity by institutions is associated with a larger actual repurchase by the firm in the subsequent period. Fourth, institutional trading has predictive power for the change in the firm's information asymmetry from before the OMR announcement to after: a larger amount of net buying by institutions is associated with a larger decrease in the degree of information asymmetry facing the firm in the equity market. Finally, institutions are able to realize significant abnormal profits (net of commissions and trading costs) by trading in the equity of firms undergoing open market repurchases. Overall, our results are consistent with the notion that institutional investors are able to generate a significant information advantage for themselves about firms undertaking OMR programs.
Sensitivity to investor sentiment and stock performance of open market share repurchases

This paper finds that stocks of repurchasers with high sensitivity to investor sentiment are more likely to be mispriced. Thus, such repurchases are followed by superior post buyback stock performance. This abnormal return associated with sensitivity to sentiment cannot be explained by other undervaluation factors: book to market or prior return effects. My results are robust with factor model analysis and controls for contamination effects. I conclude that this sentiment driven undervaluation may result from the difficulty to value and/or limits to arbitrage rather than investor overreaction.
Quantification of biodiesel in petroleum diesel by 1H NMR: Evaluation of univariate and multivariate approaches

The commercially available biodiesel in Brazil is a mixture formed by two different raw materials: oleaginous plants and animal fats. Methanol is the alcohol commonly used in the transesterification of triglycerides, however, due to high ethanol production in Brazil, it can be used by manufacturers, producing a complex commercial blend. In this paper, the biodiesel content in diesel was evaluated using the hydrogen nuclear magnetic resonance (1H NMR) technique associated with univariate and multivariate calibrations. Two univariate models were evaluated. The first one used the ratio of area of the signals of olefins to aliphatics, and the second used the ratio of the area of the signals of the methyl group from the ester to the aliphatics. The multivariate model was constructed using partial least squares (PLS) regression. The results show that using the area of the signals related to the olefins method presented an accuracy similar to the PLS model. However, the multivariate method has a better analytical sensitivity and lower limits of detection and quantification. Using only part of the 1H NMR spectrum to construct the model can be less efficient than using a broader set of information encompassing the full spectrum.
Assessing the Sustainability of First Generation Ethanol for Bioethylene Production

The majority of plastic materials today are petroleum based making the plastics industry is one of the drivers of oil extraction. To portray a greener image and decrease the industry's need for oil, some plastics producers have started using bioethanol as a feedstock. Within the next five years, the demand for bioplastics is expected to increase by about 19 increasing the demand for ethanol. Traditionally, ethanol is mainly used as a sustainable alternative to fossil fuels. Lately however, there has been much debate regarding if ethanol really is a better alternative for the environment. This study assesses the sustainability of first generation ethanol on behalf of the petrochemical company Borealis AB. The company is looking into the possibility to produce ethanol based biopolyethylene. Borealis AB is part of the project Locally Grown Plastics, aiming to produce packaging material from second generation ethanol from Swedish forestry residues. This project is still 5 10 years away from realization, however, Borealis AB are considering commencing their production of bioplastics earlier, using first generation ethanol. Scientific literature, certification schemes, and a study of actors in the ethanol industry, show that ethanol can be produced in a manner that makes it a more sustainable option for plastics production than oil. To assess the sustainability of ethanol producers in Brazil, the US and Africa, a framework was constructed. After applying the framework, comparing different feedstocks for ethanol, and looking at the certified biomass potential, three Brazilian suppliers were deemed most promising. These are recommended as the best options for Borealis, should they decide to produce bioplastics from first generation ethanol. It is concluded that first generation ethanol can be a viable choice of feedstock for a European bioplastics producer, such as Borealis AB. However, appropriate and credible certification must obtained by the ethanol suppliers to ensure the sustainability of their practice. Brazilian sugarcane ethanol is found to be the most sustainable alternative and three companies operating in the area are recommended as suppliers for Borealis AB.
Probe for the measurement of cell surface pH in vivo and ex vivo
Venue: Proceedings of the National Academy of Sciences

In acidic, diseased tissues, such as tumors, cell surfaces are expected to be more acidic than the surroundings, but no means have been available to measure surface acidity specifically in such tissues. We have developed a probe that uses a pH low insertion peptide to locate a pH sensing dye, seminaphtharhodafluor, at acidic cell surfaces and find that the probe can measure surface pH, which is correlated with tumor aggressiveness. Even an individual cancer cell maintains acidity at its surface and can be detected in a well perfused area. The new probe can be used in vivo and ex vivo on tissue specimens and opens a path to both medical utility and exploration of other functions of acidity on cell surfaces. We have developed a way to measure cell surface pH by positioning a pH sensitive fluorescent dye, seminaphtharhodafluor (SNARF) conjugated to the pH low insertion peptide (pHLIP) It has been observed that many diseased tissues are acidic and that tumors are especially so. A combination of effects acidifies tumor cell interiors, and cells pump out lactic acid and protons to maintain intracellular pH, acidifying the extracellular space. Overexpression of carbonic anhydrases on cell surfaces further contributes to acidification. Thus, the pH near tumor cell surfaces is expected to be low and to increase with distance from the membrane, so bulk pH measurements will not report surface acidity. Our new surface pH measurement tool was validated in cancer cells grown in spheroids, in mouse tumor models in vivo, and in excised tumors. We found that the surface pH is sensitive to cell glycolytic activity: the pH decreases in high glucose and increases if glucose is replaced with nonmetabolized deoxyglucose. For highly metastatic cancer cells, the pH measured at the surface was 6.7 6.8, when the surrounding external pH was 7.4. The approach is sensitive enough to detect 0.2 0.3 pH unit changes in vivo in tumors induced by i.p. injection of glucose. The pH at the surfaces of highly metastatic cells within tumors was found to be about 6.1 6.4, whereas in nonmetastatic tumors, it was 6.7 6.9, possibly creating a way to distinguish more aggressive from less aggressive tumors. Other biological roles of surface acidity may be found, now that targeted measurements are possible.
2nd International Workshop on Dynamic Resource Allocation and Management in Embedded, High Performance and Cloud Computing (DREAMCloud 2016)
Venue: ArXiv

This volume represents the proceedings of the 2nd International Workshop on Dynamic Resource Allocation and Management in Embedded, High Performance and Cloud Computing (DREAMCloud 2016) co located with HiPEAC 2016 on 19th January 2016 in Prague, Czech Republic.