dataesr/scientific-paragraphs-categorization huggingface dataset:
Proof. The dataset T * is subject to a cover problem and therefore, there exists an item x i, j ∈ R i C j ( j ≥ 2) that can be associated with a covered item y i, j-1 ∈ I i, j-1 where I i, j-1 = {y : y ∈ R i C j-1 and s(y, R i C j-1 ) ≥ s(x i, j , R i C j )} is the set of items in R i C j-1 having support greater than or equal to the support of x i, j. Let us assume that the items x i, j and y i, j-1 are associated together in k records in at least one of the datasets reconstructed by the inverse transformation of T *. Since y i, j-1 is a covered item, x i, j will also be associated k times with all the items covering y i, j-1 in I i, j-1. While this is correct from a privacy perspective, it cannot be considered for disassociation. In fact, these items, x i, j , y i, j-1 and the covering items in I i, j-1 are considered k-anonymous and therefore should have been allot to the record chunk R i C j-1 according to disassociation 4.
Now, if the attacker's background knowledge B consists of itemsets of size m and there exists an itemset I that contains both x i, j and y i, j-1 , {x i, j , y i, j-1 } ⊆ I, a privacy breach will occur. In fact, these m items will never be associated together in k records in any of the datasets reconstructed by the inverse transformation of T *. Figure  3b  describes six possible datasets, T 1 ,. ,T 6 , reconstructed by the inverse transformation of the disassociated dataset shown in Figure  3a. In this example, only five datasets T 1 ,. , T 5 are valid ones. 4 Vertical partitioning, creation of record chunks, preserves k-anonymous itemsets in the same record chunks. T 6 is omitted due to the cover problem and the knowl- edge of the disassociation algorithm. Given that the item e is associated with the covered item c is k = 2 times, consequently e is associated with a and b in k records and thus, according to the disassociation technique, the item e should have remained in the first record chunk R 1 C 1.
Quantitative Privacy Breach Detection Algorithm We present, in the following, the quantitative privacy breach detection algorithm to evaluate the privacy breach in a disassociated dataset based on the cover problems. The algorithm takes a disassociated dataset T * , and the attacker's background knowledge B that includes itemsets of size m and evaluates the privacy breach represented by the number of vulnerable records in the disassociated dataset T *. It iterates, in Step 3, over each cluster R i in T * in an ascending order to evaluate, for each item x i, j in record chunk R i C j , the number of privacy breaches in all the previous record chunks. To do so, the algorithm 1) retrieves in Step 10 the itemset I i,l of items having support greater than or equal to the support of x i, j and 2) verifies in Step 11 whether x i, j and any item y in I i,l is subject to a cover problem.
In fact, as discussed in Subsection 5. 2, a privacy breach is found not only when completely de-anonymizing a record but also when an attacker is able to link more than m items to the individual. This is noted in the algorithm every time the items involved in the association that led to the cover problem are part of the attacker's knowledge. We conducted a series of experiments on real datasets described in  The tests were performed using three types of attackers, strong, moderate and weak, where each of which has a background knowledge simulated as follows: Strong attacker : the background knowledge of this attacker consists of itemsets of size 2 (m = 2) picked from the 2-combinations of each and every record in the dataset. Since it is a strong attacker, we assume that these itemsets contain the items that are involved in the association that will lead to a cover problem (i.
e. , well picked). Therefore, a privacy breach occurs whenever a cover problem is found. Moderate attacker : the background knowledge of this attacker consists of itemsets of size 2 (m = 2) chosen at random from the 2-combinations of a strict subset of D (records in T ). I i,l ; /**I i,l = {y : y ∈ R i C l and s(y, R i C l ) ≥ s(x i, j , R i C j )}* Weak attacker : to simulate the background knowledge of this attacker, we have chosen at random 10 items from the dataset (for each test) and another 10 items from wordnet  (Miller, 1995)  and generated their 2-combinations. This gave us a background knowledge B of size 190. Privacy Breach Evaluation In this test, we evaluate the privacy breach caused by the cover problem using the quantitative privacy breach detection algorithm. Three test cases were studied.
In each of the test cases, the algorithm evaluates the aforementioned datasets by varying the maximum cluster size with fixed k = 3, m = 2 according to the attacker's background knowledge. The results are shown in Figure  4. It is not surprising that for strong attackers the privacy breach is most explicit. In fact, strong attackers' background knowledge, consisting of all the itemsets of size m = 2, are linked to each of the individuals in the dataset. Therefore, whenever a cover problem is found, a privacy breach is noted. In addition, we can notice that for the strong attacker as exhibited in the three datasets, the privacy breach increases linearly with the cluster size. Larger clusters include more cover problems and thus more privacy breaches. This is not reflected by moderate and weak attackers.
These attackers have background knowledge generated from a strict subset of randomly chosen items from the dataset and other items from external sources. Moreover and unlike the strong attackers' background knowledge, moderate and weak attackers' background knowledge are linked to a subset of records in the datasets. This fact justifies the low number of violations found in the dataset for these attackers. Performance Evaluation Here, we evaluate the performance of the quantitative privacy breach detection algorithm on the BMS-WebView-1 dataset for the three types of attackers; strong, moderate and weak. The results shown in Figure  5  demonstrate that the algorithm performs in a generally stable fashion when increasing the cluster size. The best performance is obtained when dealing with strong attackers.
Conclusion In this article, we have proposed a formal model of the set-valued data and the disassociation technique. The cover problem has been defined, while its effects on the disassociation of the dataset has been regarded. Such investigations have led to the quantitative privacy breach detection algorithm, whose efficiency has been studied. By this way, we have shown in what extent a disassociated dataset can be vulnerable. In the near future, we intend to develop partial suppression in disassociated dataset and evaluate the gain in utility that the disassociation provides.
Speech recognition and NLP are the basis for such widely used personal assistants as Apple's Siri, Microsoft's Cortana, and Google Now. While a lot of work has already been done on natural language processing, the research usually concerns widely used languages, such as English. Consequently, natural language processing in languages other than English is a very relevant subject. The paper by Gatis Špats and Ilze Birzniece "Opinion Mining in Latvian Text Using Semantic Polarity Analysis and Machine Learning Approach" is a part of this trend. The authors present main obstacles and challenges for efficient opinion mining of texts written in Latvian, and present results of using two methods for opinion mining: lexicon based method and machine learning method based on Naive Bayes classifier. For the purpose of this paper, authors have prepared a large dataset of annotated Latvian words and made it publicly available.
Following the trend of natural language processing of non-English languages, the paper "REDEN: Named-Entity Linking in Digital Literary Editions Using Linked Data Sets" by Carmen Brando et al. tackles the problem of Named-entity Linking of authors' names in French literary criticism and essays from the 19th-20th centuries. These names are disambiguated and annotated with links to broad-coverage and domain-specific Linked Data sets, DBpedia and BnF. It is also worth to mention that while the presented algorithm was used in the French literature context, it is much more generic. Moreover, the implementation of the algorithm is available as open source code. A fifth paper in this issue: "An Approach for Multi-Artifact Testing Through an Ontological Perspective for Behavior-Driven Development" by Thiago Rocha Silva et al.
Background: There is an increasing number of meta-analyses including data from non-randomized studies for therapeutic evaluation. We aimed to systematically assess the methods used in meta-analyses including nonrandomized studies evaluating therapeutic interventions. Methods: For this methodological review, we searched MEDLINE via PubMed, from January 1, 2013 to December 31, 2013 for meta-analyses including at least one non-randomized study evaluating therapeutic interventions. Etiological assessments and meta-analyses with no comparison group were excluded. Two reviewers independently assessed the general characteristics and key methodological components of the systematic review process and meta-analysis methods. Results: One hundred eighty eight meta-analyses were selected: 119 included both randomized controlled trials (RCTs) and non-randomized studies of interventions (NRSI) and 69 only NRSI.
Data extraction The data extraction form for this methodological review was developed from the MOOSE statement for reporting meta-analyses that include observational studies  [29] , the PRISMA statement for reporting systematic reviews and meta-analyses of studies evaluating healthcare interventions  [30, 31] , and the AMSTAR measurement tool for assessing the methodological quality of systematic reviews  [32]. The data extraction form was tested by one reviewer (TF) with 10 studies before data extraction commenced. Two reviewers (TF, CR) independently extracted all data in duplicate, resolving discrepancies with a third reviewer (AD) if necessary. The following characteristics were extracted from the full text and online appendix of each meta-analysis: General characteristics: We collected whether the journal was a specialty or a general journal, the location of the corresponding author, and the medical area.
Systematic review methods: ▪ Search strategy: We collected how many and which electronic databases were searched, and whether the search strategy for at least one database was provided. We collected whether reference lists and journals were hand-searched and whether the authors searched for grey literature, and if yes, how: search of registries (eg, ClinicalTrials. gov), conference abstracts, or contacting experts. We assessed whether the authors restrict their searches by language. ▪ Study selection and data extraction process: We assessed whether study selection and data extraction were conducted in duplicate. ▪ Contact of the study authors: We noted whether it was mentioned that study authors were contacted for clarification or additional results. ▪ Methodological quality/risk of bias assessment: We assessed whether methodological quality or risk of bias assessment was conducted, what tools were used, and whether the assessment was conducted in duplicate.
Literature search Overall, all but one of the meta-analyses reported the search of at least 1 electronic database and 147 (78 %) reported the search of > 2 electronic databases. One third provided the search strategy for each database (n = 62, 33 %). MEDLINE, Embase, and the Cochrane Library were most frequently searched (187 [99 %], 149 [79 %], and 126 (67 %) meta-analyses, respectively). In addition to the search of electronic databases, 162 meta-analyses (86 %) reported screening the reference lists of included studies, and 12 (6 %) reported hand-searching journals. About one-third of the meta-analyses (n = 72, 38 %) reported searching for grey literature: 41 (22 %) conference abstracts, 33 (18 %) registries, and 15 (8 %) contacted experts. For 82 meta-analyses (44 %), the authors reported that they did not restrict their searches by language.
Crude or adjusted estimates used for NRSI For 131 meta-analyses (70 %), whether crude or adjusted estimates of treatment effect from the NRSI were used for the meta-analysis was unclear or not reported. For the remaining meta-analyses, the authors reported combining crude and adjusted estimates for 22  (12 %) , only adjusted estimates for 21  (11 %) , and only crude estimates for 6 (3 %). For 8 meta-analyses (4 %), the authors   33 ) 61  (32)  North-America 18 (  26 ) 33 (  28 ) 51 (  27 ) Other 3 (  4 ) 9 (  7 ) 12  (7)  Medical area Surgery 21 (  30 ) 28 (  24 ) 49 (  26 ) Cardiology 8 (  12 ) 25 (  21 ) 33  (18)  Oncology 6 (  9 ) 19 (  16 ) 25  (13)  Other 34 (  49 ) 47 (  39 ) 81 (  43   Searched for grey literature a 23 (  33 ) 49 (  41 ) 72  (38)  Conference abstracts 14 (  20 ) 27 (  23 ) 41 (  22 ) Registries (eg, ClinicalTrials.
Strengths and limitations of study To the best of our knowledge, no previous study has comprehensively assessed both key methodological components common to all systematic reviews and elements specific to the inclusion of non-randomized studies. Other studies that previously evaluated methods or reporting of systematic reviews including NRSI concentrated on the reporting of harms  [26, 28]  and on systematic reviews in psychiatric epidemiology  [25]. Our study has some limitations. The representativeness of our sample could be debated because we searched for studies in only one online database (MEDLINE), and limited our selection to meta-analyses in English. In addition, for the assessment of the methods, we depended completely on the reporting; we did not assess protocols or contact the authors if methods were not clearly reported.
However, a recent study found that for 32 % of the observational studies registered at ClinicalTrials. gov, unpublished results could be retrieved  [37]. In contrast, we found that many meta-analyses assessed reporting bias (68 %). Reviewers may have compensated for the absence of searching for grey    2 (3)  3 (3)  5 (3)  Only cohort studies 18 (  26 ) 34 (  29 ) 52 (  28 ) Including also case-control studies 18 (  26 ) 28 (  23 ) 46  (24)  Including all types of NRSI 5 (  7 ) 18 (  15 ) 23  (12)  Other 28 (  41 ) 39  (33)  67  (36)  "Observational studies" 6 (  9 ) 22 (  18 ) 28 (  15 ) "Prospective and retrospective studies" 11 (  16 ) 12 (  10 ) 23 (  12 ) "Retrospective studies" 11 (  16 ) 5 (  4 ) 16 (  9 ) Did not clearly report design for each study 43 (  62 ) 87 (  73  Fixed-effects model, unless high heterogeneity, then random-effects model 19 (  28 ) 33 (  28 ) 52  (28)  Both fixed-and random-effects models 5 (  7 ) 21 (  18 ) 26 (  14 ) Fixed-effects model 3 (  4 ) 6 (  5  literature by assessing reporting bias.
(2) The function expressed in (2) transforms the interval [0, S max ] into [P min , P max ] where the parameters P min and P max respect the following constraints 0 ≤ P min ≤ P max. P min is the minimum probability of the default presence and P max is the maximum one. P min close to 0 indicates a high confidence level that a very low amplitude peak does not correspond to a defect and P max close to 1 indicates a high confidence level that a very high amplitude peak corresponds to a defect presence. Thus, the values of P min and P max parameters may be different from one post-processing method to another. Finally, measured probabilities of default presence according to post-processing methods are gathered. Considering the assumption that the selected post-processing methods are sequentially performed and are completely independent, the fusion of data of two post-processing methods m 1 and m 2 are calculated using the following formula: P(D|m 1 , m 2 ) = (P 1 P 2 ) / [P 1 P 2 + (1 -P 1 )(1 -P 2 )] , (3) where P 1 = P(D|m 1 ) and P 2 = P(D|m 2 ).
Acknowledgments There is no conflict of interest for any of the authors. This study was financially supported by  Ajinomoto Co. , Inc. The research reported in this paper was also financially supported by grants from the  Grant-in-Aid for Scientific Research on Innovative Areas  ( 23118001 ;  Adolescent Mind and Self-Regulation ) and  Challenging Exploratory Research (25610109)  from the  Ministry of Education, Culture, Sports, Science, and Technology of JapanThe  funders had no.
Introduction This is Part II of a trilogy investigating claims that radioactive decay deviates from the fundamental exponential-decay law due to solar influences (see introduction in  [1] ). In this work, a systematic search for annual modulations in the decay rates of beta-minus (β -) emitters is performed. Decay rate measurements repeated over periods of 259 d up to 5 decades at various laboratories around the globe were collected for this purpose. The project encompasses different types of radioactive decay, including alpha decay presented in Part I  [1]  and beta plus/ electron capture decay discussed in Part III  [2]. All data sets were checked for annual oscillations and the amplitude and phase of the best matching sinusoidal function to the residuals from exponential decay were tabulated and published in  [3].
85 Kr @NIST A series of 98 decay rate measurements were performed of a 85 Kr source in the NIST IC 'A' from 1980 until 2007. As can be seen from the residuals to the fitted exponential curve (figure  19 ) and the annual averaged data (figure  20 ), the measurements are not evenly distributed in time and do not cover every period in the year. The amplitude of the fitted sinusoidal remains nevertheless well below the permille level (A = 0. 036 (15)%, a = 153 d).
90 Sr-90 Y @PTB (#1) From the end of 1989 until 2016, in total 2207 measurements were performed of a 90 Sr solid source in the IG12/A20 IC at the PTB. Since the data set showed trending behaviour over different multi-annual periods of time, the data were subdivided in five time regions and realigned through a linear transformation in each region. By doing so, possible annual modulations would remain visible in the residuals to an exponential fit, whereas the effects of long-term instabilities would be suppressed. The residuals in figure  25  are in the permille level and show some autocorrelation. The annually averaged residuals in figure  26  show a distinct but small sinusoidal seasonal  dependency (A = 0. 018 (2)%, a = 26 d) which is almost identical to the modulations observed in the decay of 137 Cs (section 9.
124 Sb @PTB From March 2007 to April 2008, the PTB performed 232 ionis ation current measurements of a 124 Sb source (from the same stock solution as the JRC) in the IG12/A20 IC, of which 226 data were selected for analysis. A small linear detrending correction was performed on the decay-corrected data obtained after November 2007, resulting in the residuals presented in figure  30. The average residuals in figure  31  confirm that there are certainly no annual modulations at the 10 -4 level (A = 0. 006 (3)%, a = 201 d).
154 Eu @PTB Europium-154 is one of the radionuclides measured in the IG12/A20 IC at the PTB covering periods before and after October 1998, i. e. the date at which the Townsend balance method for the current readout was replaced by a commercial electrometer  [31, 38, 39]. Schrader  [38]  has published raw-i. e. not relative to a reference source-residuals from exponential decay, showing the characteristic 0. 1% annual periodicity in the period 1990-1995, and far less modulation in the period 2000-2008. In figures 44 and 45, annual averaged residuals are presented for both periods, after applying linear detrending corrections over 4 time zones in the 2000-2008 data set. The annual modulations (A = 0. 081 (3)%, a = 65 d) in the 154 Eu data from 1990 to 1995 match those of the 226 Ra check source (A = 0.
Data Given the importance of livestock production among smallholders in Sub-Saharan Africa, this study addresses a crucial gap in the literature by estimating system-wide yield gaps for in these production systems. We use the term "system-wide" to convey that all farm production inputs and outputs (inclusive of both crops and livestock) are included in our yield gap estimates. Previous studies have only assessed yield gaps within crop enterprises. It also makes a unique contribution by exploring the effects of yield improvements for multiple outputs on the emission intensity of production. The data for farm production and farm attribute variables used in this study come from the IMPACTlite database prepared by International Livestock Research Institute for the Climate Change, Agriculture and Food Security programme  (Rufino et al.
, 2013) , and is based on farm household surveys conducted over the 2012 calendar year. This study focuses on seven of the nine Sub Saharan African sites in this database covering six countries. Five of the sites are in East Africa (Nyando and Wote (Kenya); Hoima (Uganda); Lushoto (Tanzania); and Borana (Ethiopia)) and two are in West Africa (Yatenga (Burkina Faso); and Kaffrine (Senegal)). The two sites in West Africa are situated at less than 350 m altitude and have annual rainfalls ranging from 400 to 800 mm, with substantial year to year variability (Table  1 ). The sites in Eastern Africa show strong spatial heterogeneity of climate and topography, with low annual rainfall in Wote and in Borana and much higher rainfall in Hoima and Lushoto. Furthermore, rainfall predictability in Eastern Africa is relatively high and helps to reduce risks of crop failure.
Local ruminant breeds predominate across the study areas, with cross bred cattle more common in parts of Lushoto, Nyando  (Rufino et al. , 2013). The average farm sizes are similarly small in most sites with the exception of Hoima and particularly Kaffrine, where the farm sizes are appreciably larger. From the survey data we constructed two aggregate output variables, one for livestock and one for crops, and five input variables including land, labour, animals, materials, and farm assets. All of the variables, except for land (ha) and labour (hours), are composites for which indices were required to aggregate their various components. For animals, tropical livestock unit (TLU) index was used to aggregate different animal types. This index takes into account the feed requirements of different animals and is therefore reflective of their varying resource requirements  (ILRI, International Livestock Research Institute, 2011; FAO, 2003).
This required the use of quantity and price data from the IMPACTlite household survey database and, compared to most economic index numbers, the Fisher index has a number of desirable statistical properties including dimensional invariance (i. e. it is independent of the units of measurement used) and proportionality (i. e. if all quantities increase by the same proportion, then the index will increase by the same proportion)  (Coelli et al. , 2005). The Fisher indexes are dimensionless quantities that are relative to a "base farm" in each sample and, as such, they have no interpretative value and do not warrant inclusion in Table  2. For the purposes of exposition we have included Fertilizer, one of main components of the Material input. Similarly the main livestock and crop products are also displayed in Table  2.
Calculation of GHG emission intensities Baseline GHG emissions from livestock (CH 4 and N 2 O from animals and manure management) and crops (N 2 O emissions from fertilizers, manures and plant residues) were calculated using Tier 1 methods outlined in Intergovernmental Panel on Climate Change (IPCC) guidelines  (IPCC, 2006). Emission intensities were then calculated by dividing the emissions from livestock and crop production by their corresponding products, expressed in terms of protein and energy equivalents, respectively. The various livestock outputs were converted into protein equivalents based on protein conversion factors from  Opio et al. (2013)  and  MacLeod et al. (2013) , and then aggregated into ruminant and poultry products. Similarly the crop products were converted into energy equivalents based on the USDA Food Composition Database (USDA, U.