bluuebunny/crossref_metadata_2025 huggingface dataset:
Avoidance of Routine Use of Episiotomy in Primigravida
Authors: Hamda Trifa , Alsakkal Ghada
Published: June 2022

Background and objective: The justification of the routine use of the episiotomy arouse at the beginning of 20th century. It was based on personal beliefs without scientific basis and this led to the incorporation of episiotomy in daily practice . High-quality methodological studies gave strong evidence that episiotomy should not be done routinely. The purpose of this study was to find out the rate and degrees of perineal tear in women with versus without episiotomy and to evaluate the severity of pain and complications including wound infection, wound dehiscence and dyspareunia. Methods: This is a prospective interventional study included 200 primigravida with term pregnancy, who attended the labor room at Maternity Teaching Hospital from 1st of January 2008 till 1st of July 2008. Episiotomy was done for a group and avoided in another group, then follow up was done to evaluate complications. Results: Perineal tears were significantly more in patients with episiotomy as episiotomy itself is regarded as second degree perineal tear. There was significant difference in the severity of pain, wound infection and dyspareunia among both groups but there was no significant difference in the Apgar score of the newborn in both group. Conclusion: This study does not supports maternal benefits of routine episiotomy.
Estimation of T-helper 1 Cytokines (IFN-y and TL-2) in HBV Infected patients and Individuals vaccinated with Recombinant HB Vaccine
Authors: Al-Barzinji Rukia , Ahmed Hiwa
Published: June 2022

Background and objective: The specific cellular immune responses play a main role in the hepatic necrosis that occurs with hepatitis B virus (HBV) infection. Interferon-g (IFN-g) and interleukin-2 (IL-2) are considered examples on T helper 1(Th1) cytokines which required for host antiviral immune response and involved in cell-mediate immunity against HBV infection. This study was designed to estimation T-helper 1 cytokines (IFN-g and IL-2) in HBV infected patients and individuals vaccinated with recombinant HB vaccine. Methods: The study groups were classified into patient group 35 (15 acute hepatitis (AH) and 20 chronic hepatitis(CH)) and 35 vaccinated group (20 responder (RD) and 15 Nonresponder (NRD)) and 18 control group, during May to November 2007. Blood samples were taken from patients and hospitals staffs in Nanakaly, Erbil and Rizgary Teaching Hospital to detect hepatitis B surface antigen (HBsAg), anti hepatitis core antibody IgM (Anti- HBc Ab(IgM)), Anti-HBs Ab, IFN-g level and IL-2 level in serum by enzyme linked immunosorbent assay (ELISA). Results: The concentration of IFN-g and IL-2 levels in the AH group differed significantly compared with healthy control and CH patients (p<0.01) by F-test. LSD-analysis for IFN-g also revealed same result while IL-2 level significantly increased in healthy control only. Ftest for IFN-g revealed (p<0.05) among RD group, NRD group and healthy non vaccinated (HN) control in ≥30 and <30 years old respectively but inverse result was observed in IL-2 levels (p>0.05).
Prevalence of Cigarette Smoking among Hawler Medical University Students
Authors: Othman Samir , Saleh Abubakir , M.Ali Khalis
Published: June 2022

Background and objective: Tobacco smoking is a growing public health problem in the developing countries. This study estimates the prevalence of smoking and assesses the socio-demographic correlates of smoking among Hawler Medical University students in Erbil city. Methods: A cross sectional study was conducted during March - April 2007 on 600 students in Hawler Medical University. A systematic stratified sampling method was used. A special questionnaire was used. Data on age and sex of the students, name of college, number of cigarette smoked per day, and age of starting smoking were obtained. Results: out of the 587 respondents, 72 students were smokers giving a prevalence rate of 12.3%.The prevalence of smokers were much higher in males than females (23.9% and 1.9%, respectively). The highest rate of smokers was among the age group 24-26 year in both sexes. 50% of students started smoking at the age of 18-22 years. Conclusion: The prevalence of smoking is comparatively higher taking into consideration that the study was conducted on students of medical and allied health sciences and that more than half of students started smoking during study years. The study recommends integrating health awareness programmes about smoking hazards in the medical education curriculum.
Effect of Hemodialysis and Peritoneal Dialysis on some Hematological and Biochemical Parameters in Renal Failure
Authors: Mohammad Dara
Published: June 2022

Background and objective: The purpose of the present study was to determine the effects of hemodialysis and peritoneal dialysis on some hematological and biochemical parameters in patients with renal failure on the dialysis. Methods: Fifty two patients with renal failure were taken in this experiment on dialysis studied in the dialysis and kidney disease center in hawler teaching hospital, twenty six patients were taken for haemodialysis and the remaining patients were taken for the peritoneal dialysis process. Some hematological and biochemical parameters were taken before and after dialysis. Results: In the Hemodialysis Patients: Hemoglobin concentration (Hb gm/dl), Red blood cells (RBCs x106/μl) and packed cell volume (PCV %) count, blood sugar (mg/dl), serum calcium (mg/dl), serum sodium (meq/L) and serum potassium (meq/L) were high significantly increased in patient with renal failure after hemodialysis in comparison before hemodialysis, while blood urea (mg/dl) were high significantly decreased in patient with renal failure after hemodialysis in comparison before hemodialysis. In the Peritoneal dialysis patients: Hb (gm/dl) and PCV (%) count, blood sugar (mg/dl) and serum calcium (mg/dl) were high significantly increased in patient with renal failure after peritoneal dialysis in comparison before peritoneal dialysis, while blood Urea (mg/dl) and serum Creatinine (mg/dl) were high significantly decreased in patient with renal failure after peritoneal dialysis in comparison before peritoneal dialysis, while there were non significant differences in serum sodium (meq/L) before and after peritoneal dialysis. Conclusion: Hemodialysis significantly increases some RBC, Hb and PCV. Hemodialysis cause significantly decreases blood sugar and urea. Peritoneal dialysis significantly increases PCV and Hb. Peritoneal dialysis cause decreases the blood urea and blood sugar. Hemodialysis was more useful and gives good results than peritoneal dialysis.
PAHANG STATE FOLKLORE BASED ON THE LEGEND OF CHINI LAKE DRAGON
Authors: Robe’ah Yusuf  , Fathiah Izzati Mohamad Fadzillah  , MOHAMAD JAMILAH BEBE , Jamal Rizal Razali
Published: June 2022

This paper discusses the famous Pahang state folklore which is the Legend Dragon of Chini Lake. This state has an extensive forest as well as breathtaking islands. Among the popular attractions are Tioman Island and Chini Lake. Chini Lake is very famous for its stories of dragons in various versions. It is said that the history of Tioman Island started with the tale of the dragon that left Chini Lake. It is believed that the story of the dragon is related to an island in Indonesia. Hence, the name of the villages on the island are similar to the names of the villages on Tioman Island such as Mukut, Genting, Juara, Salang and Paya. Apart from the similarity of the village name, the shape of both islands is also quite similar as both have sharp mountain tops such as dragon horns. The similarity of the name of the village has led to allegations that the residents of Tioman Island are from the Riau Archipelago and Khmer, Cambodia.The allegation states that the residents there are of Cambodian descent because the dialects used have little in common. Nevertheless, this unique qualities demands more in-depth research to be documented to today’s generation.
PENGARUH FAKTOR KESIHATAN MENTAL, KECERDASAN SPIRITUAL KE ATAS KEBAHAGIAAN HIDUP DAN PRESTASI AKADEMIK PELAJAR
Authors: Mat Wajar Muhammad Salehan Alhafiz , Hamzah Rohana Binti , Mohamad Ahmad Muhaimin , Christina Andin
Published: June 2022

Abstract - This paper presents an insight on the effect of mental health and spiritual intelligence on the happiness and academic performance among undergraduate students at Universiti Malaysia Pahang (UMP). The study had been conducted using quantitative method. A set of questionnaire had been distributed to 460 respondents randomly and selected from a population of 9121 UMP undergraduate students. In prior, the instrument had been checked for its validity and reliability. Results indicate majority of the respondents had achieved high level of mental health, spiritual intelligence and happiness. The correlation analysis revealed that mental health and spiritual intelligence had a significant positive effect on happiness and academic performance. The regression analysis however had shown that happiness had no significant mediation effect on the relationship between mental health and spiritual intelligence and academic performance. Overall, this study had filled in the literature gap on the level of mental health, spiritual intelligence and happiness among students in the context of Malaysian universities particularly in UMP. The model developed by the researcher can be used as a reference in future studies for further investigate how students’ happiness and academic performance can be enhanced through their mental health and spiritual intelligence.
 
Abstrak - Penyelidikan ini bertujuan untuk mengkaji pengaruh faktor kesihatan mental dan kecerdasan spiritual ke atas kebahagiaan hidup dan prestasi akademik pelajar-pelajar Ijazah Sarjana Muda di Universiti Malaysia Pahang (UMP). Kajian dijalankan secara kuantitatif melalui kaedah tinjauan dengan menggunakan soal selidik sebagai instrumen kajian ke atas 460 orang responden yang dipilih secara rawak mudah daripada 9121 orang pelajar Ijazah Sarjana Muda UMP. Analisis deskriptif mendapati majoriti responden mempunyai tahap kesihatan mental, kecerdasan spiritual dan kebahagiaan hidup yang agak tinggi. Analisis korelasi pula menunjukkan bahawa kesihatan mental dan kecerdasan spiritual berhubung secara positif dengan kebahagiaan hidup dan prestasi akademik. Analisis regresi walau bagaimanapun membuktikan bahawa kebahagiaan hidup tidak mempunyai pengaruh mediasi yang signifikan ke atas hubungan di antara kesihatan mental dan kecerdasan spiritual dengan prestasi akademik. Secara keseluruhannya, kajian ini telah menyumbang kepada kekurangan dalam literatur mengenai tahap kesihatan mental, kecerdasan spiritual dan kebahagiaan hidup dalam konteks pelajar universiti di Malaysia khususnya di UMP. Model yang dibina dalam kajian ini boleh dijadikan rujukan kajian yang lebih mendalam pada masa akan datang untuk memahami bagaimana kebahagiaan hidup dan prestasi akademik pelajar boleh ditingkatkan melalui kesihatan mental dan kekuatan kerohanian mereka.
Analisis Model Antrian Multiserver Pada Samsat Kabupaten Kabupaten Semarang
Authors: Rositawati Lusy , Rochmad Rochmad , Kartono Kartono
Published: June 2022

Tingkat kedatangan pelanggan pajak yang banyak dengan jumlah petugas pelayanan yang terbatas, sering menyebabkan Samsat Kabupaten Semarang terjadi antrian. Oleh karena itu, perlu mengetahui model sistem antrian yang saat ini diterapkan di Samsat Kabupaten Semarang, mengetahui ukuran keefektifan proses pelayanan, dan mengetahui banyaknya petugas pelayanan yang ideal. Data yang digunakan yaitu data primer yang diambil dari hasil observasi pada hari kerja dan dijam sibuk, kemudian dianalisis menggunakan SPSS serta dihitung secara manual dan dibandingkan dengan hasil yang menggunakan software WinQSB. Dari hasil analisis diperoleh bahwa sistem antrian pada Samsat Kabupaten Semarang mengikuti model sistem antrian seri majemuk dengan dua stasiun, stasiun pertama pada loket pendaftaran adalah [M/M/1]:[GD/âˆž/âˆž] dan pada stasiun kedua pada loket kasir adalah [M/M/3]:[GD/âˆž/âˆž]. Pelayanan pelanggan pajak pada loket pendaftaran Samsat Kabupaten Semarang belum efektif, sedangkan pada loket kasir sudah efektif. Jumlah petugas pelayanan pada loket pendaftaran belum ideal sedangkan pada loket kasir sudah ideal.
Penerapan Metode Fuzzy Mamdani dan Metode Fuzzy Sugeno dalam Penentuan Jumlah Produksi
Authors: Muflihunna Khaerunissa , Mashuri Mashuri
Published: June 2022

This study examines the application of the Mamdani fuzzy method and Sugeno fuzzy method in determining the amount of jenang production, based on the variables of demand, supply, raw materials and production. The purpose of this study was to apply the Mamdani fuzzy and Sugeno fuzzy methods, and to find out which method is more accurate between the two methods in determining the amount of jenang production based on the MAPE value. Data collection was obtained at PJ Menara Kudus with Jenang Kombinasi and Jenang Wijen products in January 2020 to March 2021. The results of data processing obtained MAPE values for jenang kombinasi products using the Mamdani fuzzy method is 7,5% while the Sugeno fuzzy method is 7,1%, and the MAPE value for jenang wijen product using Mamdani fuzzy method is 21,7% while the Sugeno fuzzy method is 8,2%. Based on the result obtained, the MAPE value of Sugeno fuzzy method is less than Mamdani fuzzy method. So it can be concluded that Sugeno fuzzy method more accurate than Mamdani fuzzy method in determining the amount of jenang production at PJ Menara Kudus.
Desain Algoritma Kriptografi Klasik dengan Konsep Koset Grup dalam Teori Aljabar
Authors: Yulianti Nia , Wijaya Angga
Published: June 2022

Penelitian ini mengkaji konsep koset grup apabila diterapkan pada kriptografi dalam mengkodekan karakter yang ada pada plaintext ke beberapa kemungkinan ciphertext yang berada di koset yang sama. Kelebihan menggunakan koset untuk partisi yaitu dapat mengetahui apakah dua anggota dalam suatu grup terletak pada suatu bagian partisi yang sama atau tidak. Penelitian ini menggunakan metode kuantitatif dengan dua pendekatan, yaitu studi literatur dan menyajikan contoh enkripsi deskripsi dilengkapi dengan simulasi program bahasa C++. Perbandingan karakter plaintext dan ciphertext pada pengujian recovery menggunakan percobaan satu sampel dengan plaintext dari teks dalam bahasa Inggris berukuran 48156 huruf dengan kunci k=3 diperoleh kesamaan 100 persen atau identik. Dengan demikian algoritma enkripsi dan dekripsi dapat dikatakan berhasil memenuhi syarat recovery plaintext. Berdasarkan penelitian dan pengujian yang telah dilakukan, dapat disimpulkan bahwa hasil pengujian analisis frekuensi pada enkripsi kriptografi klasik dengan penerapan konsep koset grup tidak terlihat hubungan antara sebaran kemunculan huruf di plaintext dengan kemunculan huruf di ciphertext, sehingga kriptanalis tidak dapat menerka aturan enkripsi yang digunakan.
Robust interpolation of sequences with periodically stationary multiplicative seasonal increments
Authors: Luz M.M. , Moklyachuk M.P.
Published: June 2022

We consider stochastic sequences with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. We solve the interpolation problem for linear functionals constructed from unobserved values of a stochastic sequence of this type based on observations of the sequence with a periodically stationary noise sequence. For sequences with known matrices of spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal interpolation of the functionals. Formulas that determine the least favorable spectral densities and the minimax (robust) spectral characteristics of the optimal linear interpolation of the functionals are proposed in the case where spectral densities of the sequences are not exactly known while some sets of admissible spectral densities are given.
Fixed point theorems on an orthogonal metric space using Matkowski type contraction
Authors: Singh B. , Singh V. , Uddin I. , Acar Ö.
Published: June 2022

The purpose of this paper is to prove Boyd-Wong and Matkowski type fixed point theorems in orthogonal metric space which was defined by M.E. Gordji in 2017 and is an extension of the metric space. Some examples are established in support of our main results. Finally, we apply our results to establish the existence of a unique solution of a periodic boundary value problem.
On statistically convergent complex uncertain sequences
Authors: Das B. , Debnath P. , Tripathy B.C.
Published: June 2022

In this paper, we extend the study of statistical convergence of complex uncertain sequences in a given uncertainty space. We produce the relation between convergence and statistical convergence in an uncertain environment. We also initiate statistically Cauchy complex uncertain sequence to prove that a complex uncertain sequence is statistically convergent if and only if it is statistically Cauchy. We further characterize a statistically convergent complex uncertain sequence via boundedness and density operator.
The Impact of Paternalistic Leadership on Innovative Work Behavior. A Test of Mediation Model.
Authors: Patel Tayaba , Bakari Haroon
Published: June 2022

Leadership is a paramount factor in enhancing employee innovation and creativity. This study aims to test the impact of paternalistic leadership (PL) on employees’ innovative work behavior (IWB) and to check the mediating role of employee Psychological Safety (PS). Using positivistic philosophy and a deductive approach, this study has employed a convenience sampling technique to collect data through questionnaires from 317 employees working in the Textile and IT industries. Partial Least Square Structural equation modeling (PLS-SEM) is used to analyze the data. Findings suggest that psychological safety was positively related to innovative work behavior and psychological safety mediated this link. This study is among very few studies that have tested the paternalistic leadership style in the innovative context of Pakistan. A major contribution for practitioners is to know that paternalistic leadership will enhance employee innovative work behavior through psychological safety. Moreover, data from the textile and software industries also increased the relevance and originality of this study.JEL Classification: J24, J28, O31, L67, L86
Purchase Intention of Halal Cosmetics: The Mediating Role of Attitude
Authors: Maulani Maghfira Rizky , Nuryakin Nuryakin , Hidayah Nur
Published: June 2022

This study investigates the effect of knowledge, religiosity, andattitude on purchase intention. A more comprehensive studymodel is considered an effort to add to the literature on the studyof halal cosmetics, which is still very limited amid the progress ofthe halal cosmetics industry in Indonesia. The millennial samplewas used in this study and then analyzed using SEM. Accordingto the result, knowledge did not significantly impact purchaseintentions but played an essential role in influencing attitudes.Meanwhile, religiosity has been proven to significantly impact andcontribute the most to determining purchase intention but wasinsignificant in influencing attitudes. Finally, the result also foundthat attitudes mediated the influence of knowledge and religiosityon purchase intention. For this reason, industry players shouldpay more attention to religiosity in promoting their products.Consumers must be given additional knowledge to influencepositive consumer behavior, encouraging purchase intention. Theimplication of this study is limited to the millennial sample ofthe halal cosmetics product in Indonesia.JEL Classification: M31, M37
Tax Avoidance Mediated by Constitutional Ownership as Moderating Variables
Authors: Widadi Feryal Amima , Subroto Bambang , Rahman Aulia Fuad
Published: June 2022

This study analyzes Tax Avoidance Mediated by InstitutionalOwnership as a Moderating Variable. The analytical methodused is Partial Least Square (PLS), with a sample of seventysevenfood and beverage manufacturing companies listed on theIDX for 2014 - 2020. The findings of this study show thatthin capitalization, profitability, and return on assets (ROA) ontax avoidance are influenced by institutional ownership. Thiscondition is one of the challenging issues to overcome in termsof tax avoidance for manufacturing companies in the food andbeverage sector of the food and beverage sector listed on theIndonesia Stock Exchange. The results of this study can be usedas a reference in making decisions for company owners andmanagers. Before investing their shares, investors will evaluatewhether tax avoidance by the company will provide benefits toovercome the tax burden or vice versa.JEL Classification: H26, E43, D24, G32
THE DYNAMICS OF GOVERNMENT POLITICAL POLICY	TO AGAMA DJAWA SUNDA (ADS); Study of Historical Analysis and Its Development
Authors: Sanusi Anwar , Rizaldy Azmi
Published: June 2022

Sadewa Alibassa Koesoema Widjajaningrat or known as Madrais was born in Kuningan and had studied religious insight at several Islamic boarding schools. However, the pattern of his life was dominated by mysticism, so that he feels he has received revelation from God. It was on the basis of receiving this revelation that Madrais founded the Agama Djawa Sunda (ADS). The Agama Djawa Sunda (ADS) is one of the local belief systems in Indonesia and had developed in the Cigugur, Kuningan and several other areas in West Java. In its development, it often experiences obstacles and resistances. Even so, its adherents are still able to maintain the Agama Djawa Sunda (ADS) and move according to the times. The basic conception of Javanese-Sundanese belief lies on three main pillars, namely the Concept of God, Pikukuh Tilu and Customary Law (Circumcision, Marriage and Death) according to the teachings developed in ADS.
Yeni Toplumsal Hareketlerde Kimlik Taleplerinin Dönüşümü
Authors: AVCİL Ceren
Published: June 2022

Kimlik konusu küreselleşme, ekonominin yapısal dönüşümü vb. süreçlerine paralel bir biçimde geniş kitlelerin farklı talepleri doğrultusunda yeniden şekillenmeye başlamıştır. Kimliğin evrensel ve özcü yaklaşımına karşılık farklılık temelinde ele alınması gerektiğine dair görüşler dillendirilmeye başlamıştır. Her kimliğin kendine özgü koşullar içerisinde ele alınması fikri, “öteki” olarak nitelendirilen kimliklerin de görünür olmasına imkân sağlamıştır. Bu minvalde kimlik geleneksel algısının ötesinde ve farklılıkları da içerecek şekilde daha kapsamlı bir şekilde ele alınmaya başlamıştır. Çevre hareketleri, barış hareketleri, kadın hareketleri vb. hareketler, ulus ötesine varan nitelikleri ile farklı coğrafya ve ideolojiden bireylerin ortak söylem, dil ve talepler doğrultusunda hareket ederek katılımcı diyalog zemininin oluşmasını sağlamıştır. Kimlik, yeni toplumsal hareketler ile birlikte siyasal bir talep şeklinde, kamusal hayatın merkezine yerleşmiştir.Bu çalışmada, eski ve yeni toplumsal hareketler kavramlarına yönelik literatür taraması yapılmış ve hareketler karşılaştırmalı olarak ele alınmıştır. Sonrasında kimliğin bu hareketler ile yaşadığı dönüşüme odaklanılmıştır. Yeni toplumsal hareketler ile yeniden şekillenen kimlik, farklılıklar ile birlikte ötekilik meselesini de gündeme getirmiş, kimliklerin homojen yapısından ziyade çok parçalılık ve çeşitlilik temelinde yeniden düşünülmesini gündeme getirmiştir. Kimliğin bu yapısı, taleplerin siyasallaşmasına ivme kazandıran bir gelişme olarak değerlendirilmektedir. Çevre ve kadın hareketleri, yeni toplumsal hareketler çerçevesinde farklı kimlik taleplerinin gündem olmasına zemin hazırlamıştır.
Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds
Authors: Jiang Meng , Cui Peng , Yuan Nicholas Jing , Xie Xing , Yang Shiqiang
Published: June 2022

People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fully-overlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially overlapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPTrans. To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user's behaviors in different platforms. Extensive experiments across two real social networks show that XPTrans significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPTrans can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms.
Maximizing Revenue with Limited Correlation: The Cost of Ex-Post Incentive Compatibility
Authors: Albert Michael , Conitzer Vincent , Lopomo Giuseppe
Published: June 2022

In a landmark paper in the mechanism design literature, Cremer and McLean (1985) (CM for short) show that when a bidder’s valuation is correlated with an external signal, a monopolistic seller is able to extract the full social surplus as revenue. In the original paper and subsequent literature, the focus has been on ex-post incentive compatible (or IC) mechanisms, where truth telling is an ex-post Nash equilibrium. In this paper, we explore the implications of Bayesian versus ex-post IC in a correlated valuation setting. We generalize the full extraction result to settings that do not satisfy the assumptions of CM. In particular, we give necessary and sufficient conditions for full extraction that strictly relax the original conditions given in CM. These more general conditions characterize the situations under which requiring ex-post IC leads to a decrease in expected revenue relative to Bayesian IC. We also demonstrate that the expected revenue from the optimal ex-post IC mechanism guarantees at most a (|Θ| + 1)/4 approximation to that of a Bayesian IC mechanism, where |Θ| is the number of bidder types. Finally, using techniques from automated mechanism design, we show that, for randomly generated distributions, the average expected revenue achieved by Bayesian IC mechanisms is significantly larger than that for ex-post IC mechanisms.
An Oral Exam for Measuring a Dialog System’s Capabilities
Authors: Cohen David , Lane Ian
Published: June 2022

This paper suggests a model and methodology for measuring the breadth and flexibility of a dialog system's capabilities. The approach relies on having human evaluators administer a targeted oral exam to a system and provide their subjective views of that system's performance on each test problem. We present results from one instantiation of this test being performed on two publicly-accessible dialog systems and a human, and show that the suggested metrics do provide useful insights into the relative strengths and weaknesses of these systems. Results suggest that this approach can be performed with reasonable reliability and with reasonable amounts of effort. We hope that authors will augment their reporting with this approach to improve clarity and make more direct progress toward broadly-capable dialog systems.
Look-Ahead with Mini-Bucket Heuristics for MPE
Authors: Dechter Rina , Kask Kalev , Lam William , Larrosa Javier
Published: June 2022

The paper investigates the potential of look-ahead in the con-text of AND/OR search in graphical models using the Mini-Bucket heuristic for combinatorial optimization tasks (e.g., MAP/MPE or weighted CSPs). We present and analyze the complexity of computing the residual (a.k.a Bellman update) of the Mini-Bucket heuristic and show how this can be used to identify which parts of the search space are more likely to benefit from look-ahead and how to bound its overhead. We also rephrase the look-ahead computation as a graphical model, to facilitate structure exploiting inference schemes. We demonstrate empirically that augmenting Mini-Bucket heuristics by look-ahead is a cost-effective way of increasing the power of Branch-And-Bound search.
Implementing Troubleshooting with Batch Repair
Authors: Stern Roni , Kalech Meir , Shinitzky Hilla
Published: June 2022

Recent work has raised the challenge of efficient automated troubleshooting in domains where repairing a set of components in a single repair action is cheaper than repairing each of them separately. This corresponds to cases where there is a non-negligible overhead to initiating a repair action and to testing the system after a repair action. In this work we propose several algorithms for choosing which batch of components to repair, so as to minimize the overall repair costs. Experimentally, we show the benefit of these algorithms over repairing components one at a time.
Causal Explanation Under Indeterminism: A Sampling Approach
Authors: Merck Christopher , Kleinberg Samantha
Published: June 2022

One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. However, most methods for explanation of specific events have provided theoretical approaches with limited applicability. In contrast we make two main contributions: an algorithm for explanation that calculates the strength of token causes, and an evaluation based on simulated data that enables objective comparison against prior methods and ground truth. We show that the approach finds the correct relationships in classic test cases (causal chains, common cause, and backup causation) and in a realistic scenario (explaining hyperglycemic episodes in a simulation of type 1 diabetes).
Logical Foundations of Privacy-Preserving Publishing of Linked Data
Authors: Cuenca Grau Bernardo , Kostylev Egor
Published: June 2022

The widespread adoption of Linked Data has been driven by the increasing demand for information exchange between organisations, as well as by data publishing regulations in domains such as health care and governance. In this setting, sensitive information is at risk of disclosure since published data can be linked with arbitrary external data sources. In this paper we lay the foundations of privacy-preserving data publishing (PPDP) in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of safe and optimal anonymisations. Safety ensures that the anonymised data can be published with provable protection guarantees against linking attacks, whereas optimality ensures that it preserves as much information from the original data as possible, while satisfying the safety requirement. We establish the complexity of the underpinning decision problems both under open-world semantics inherent to RDF and a closed-world semantics, where we assume that an attacker has complete knowledge over some part of the original data.
SAT-to-SAT: Declarative Extension of SAT Solvers with New Propagators
Authors: Janhunen Tomi , Tasharrofi Shahab , Ternovska Eugenia
Published: June 2022

Special-purpose propagators speed up solving logic programs by inferring facts that are hard to deduce otherwise. However, implementing special-purpose propagators is a non-trivial task and requires expert knowledge of solvers. This paper proposes a novel approach in logic programming that allows (1) logical specification of both the problem itself and its propagators and (2) automatic incorporation of such propagators into the solving process. We call our proposed language P[R] and our solver SAT-to-SAT because it facilitates communication between several SAT solvers. Using our proposal, non-specialists can specify new reasoning methods (propagators) in a declarative fashion and obtain a solver that benefits from both state-of-the-art techniques implemented in SAT solvers as well as problem-specific reasoning methods that depend on the problem's structure. We implement our proposal and show that it outperforms the existing approach that only allows modeling a problem but does not allow modeling the reasoning methods for that problem.
Scalable Training of Markov Logic Networks Using Approximate Counting
Authors: Sarkhel Somdeb , Venugopal Deepak , Pham Tuan , Singla Parag , Gogate Vibhav
Published: June 2022

In this paper, we propose principled weight learning algorithms for Markov logic networks that can easily scale to much larger datasets and application domains than existing algorithms. The main idea in our approach is to use approximate counting techniques to substantially reduce the complexity of the most computation intensive sub-step in weight learning: computing the number of groundings of a first-order formula that evaluate to true given a truth assignment to all the random variables. We derive theoretical bounds on the performance of our new algorithms and demonstrate experimentally that they are orders of magnitude faster and achieve the same accuracy or better than existing approaches.
Target Surveillance in Adversarial Environments Using POMDPs
Authors: Egorov Maxim , Kochenderfer Mykel , Uudmae Jaak
Published: June 2022

This paper introduces an extension of the target surveillance problem in which the surveillance agent is exposed to an adversarial ballistic threat. The problem is formulated as a mixed observability Markov decision process (MOMDP), which is a factored variant of the partially observable Markov decision process, to account for state and dynamic uncertainties. The control policy resulting from solving the MOMDP aims to optimize the frequency of target observations and minimize exposure to the ballistic threat. The adversary’s behavior is modeled with a level-k policy, which is used to construct the state transition of the MOMDP. The approach is empirically evaluated against a MOMDP adversary and against a human opponent in a target surveillance computer game. The empirical results demonstrate that, on average, level 3 MOMDP policies outperform lower level reasoning policies as well as human players.
Detection of Plan Deviation in Multi-Agent Systems
Authors: Banerjee Bikramjit , Loscalzo Steven , Thompson Daniel
Published: June 2022

Plan monitoring in a collaborative multi-agent system requires an agent to not only monitor the execution of its own plan, but also to detect possible deviations or failures in the plan execution of its teammates. In domains featuring partial observability and uncertainty in the agents’ sensing and actuation, especially where communication among agents is sparse (as a part of a cost-minimized plan), plan monitoring can be a significant challenge. We design an Expectation Maximization (EM) based algorithm for detection of plan deviation of teammates in such a multi-agent system. However, a direct implementation of this algorithm is intractable, so we also design an alternative approach grounded on the agents’ plans, for tractability. We establish its equivalence to the intractable version, and evaluate these techniques in some challenging tasks.
Fast Hybrid Algorithm for Big Matrix Recovery
Authors: Zhou Tengfei , Qian Hui , Shen Zebang , Xu Congfu
Published: June 2022

Large-scale Nuclear Norm penalized Least Square problem (NNLS) is frequently encountered in estimation of low rank structures. In this paper we accelerate the solution procedure by combining non-smooth convex optimization with smooth Riemannian method. Our methods comprise of two phases. In the first phase, we use Alternating Direction Method of Multipliers (ADMM) both to identify the fix rank manifold where an optimum resides and to provide an initializer for the subsequent refinement. In the second phase, two superlinearly convergent Riemannian methods: Riemannian NewTon (NT) and Riemannian Conjugate Gradient descent (CG) are adopted to improve the approximation over a fix rank manifold. We prove that our Hybrid method of ADMM and NT (HADMNT) converges to an optimum of NNLS at least quadratically. The experiments on large-scale collaborative filtering datasets demonstrate very competitive performance of these fast hybrid methods compared to the state-of-the-arts.
Deep Neural Networks for Learning Graph Representations
Authors: Cao Shaosheng , Lu Wei , Xu Qiongkai
Published: June 2022

In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the sampling-based method for generating linear sequences proposed by Perozzi et al. (2014). The advantages of our approach will be illustrated from both theorical and empirical perspectives. We also give a new perspective for the matrix factorization method proposed by Levy and Goldberg (2014), in which the pointwise mutual information (PMI) matrix is considered as an analytical solution to the objective function of the skip-gram model with negative sampling proposed by Mikolov et al. (2013). Unlike their approach which involves the use of the SVD for finding the low-dimensitonal projections from the PMI matrix, however, the stacked denoising autoencoder is introduced in our model to extract complex features and model non-linearities. To demonstrate the effectiveness of our model, we conduct experiments on clustering and visualization tasks, employing the learned vertex representations as features. Empirical results on datasets of varying sizes show that our model outperforms other stat-of-the-art models in such tasks.
Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data
Authors: Liu Zitao , Hauskrecht Milos
Published: June 2022

Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate that our approach is superior on the prediction tasks for multivariate, irregularly sampled clinical time series, and it outperforms both the population based and patient-specific time series prediction models in terms of prediction accuracy.
Representing Sets of Instances for Visual Recognition
Authors: Wu Jianxin , Gao Bin-Bin , Liu Guoqing
Published: June 2022

In computer vision, a complex entity such as an image or video is often represented as a set of instance vectors, which are extracted from different parts of that entity. Thus, it is essential to design a representation to encode information in a set of instances robustly. Existing methods such as FV and VLAD are designed based on a generative perspective, and their performances fluctuate when difference types of instance vectors are used (i.e., they are not robust). The proposed D3 method effectively compares two sets as two distributions, and proposes a directional total variation distance (DTVD) to measure their dissimilarity. Furthermore, a robust classifier-based method is proposed to estimate DTVD robustly, and to efficiently represent these sets. D3 is evaluated in action and image recognition tasks. It achieves excellent robustness, accuracy and speed.
Wishart Mechanism for Differentially Private Principal Components Analysis
Authors: Jiang Wuxuan , Xie Cong , Zhang Zhihua
Published: June 2022

We propose a new input perturbation mechanism for publishing a covariance matrix to achieve (epsilon,0)-differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, we apply this mechanism to principal component analysis (PCA). Our mechanism is able to keep the positive semi-definiteness of the published covariance matrix. Thus, our approach gives rise to a general publishing framework for input perturbation of a symmetric positive semidefinite matrix. Moreover, compared with the classic Laplace mechanism, our method has better utility guarantee. To the best of our knowledge, the Wishart mechanism is the best input perturbation approach for (epsilon,0)-differentially private PCA. We also compare our work with previous exponential mechanism algorithms in the literature and provide near optimal bound while having more flexibility and less computational intractability.
Random Composite Forests
Authors: DeSalvo Giulia , Mohri Mehryar
Published: June 2022

We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected out of a hypothesis set composed of p subfamilies with different complexities. We prove new data-dependent learning guarantees for this family in the multi-class setting. These learning bounds provide a quantitative guidance for the choice of the hypotheses at each leaf. Remarkably, they depend on the Rademacher complexities of the sub-families of the predictors and the fraction of sample points correctly classified at each leaf. We further introduce random composite trees and derive learning guarantees for random composite trees which also apply to Random Forests. Using our theoretical analysis, we devise a new algorithm, RANDOMCOMPOSITEFORESTS (RCF), that is based on forming an ensemble of random composite trees. We report the results of experiments demonstrating that RCF yields significant performance improvements over both Random Forests and a variant of RCF in several tasks.
DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays
Authors: Zhe Shandian , Qi Yuan , Park Youngja , Xu Zenglin , Molloy Ian , Chari Suresh
Published: June 2022

Tensor decomposition methods are effective tools for modelling multidimensional array data (i.e., tensors). Among them, nonparametric Bayesian models, such as Infinite Tucker Decomposition (InfTucker), are more powerful than multilinear factorization approaches, including Tucker and PARAFAC, and usually achieve better predictive performance. However, they are difficult to handle massive data due to a prohibitively high training cost. To address this limitation, we propose Distributed infinite Tucker (DinTucker), a new hierarchical Bayesian model that enables local learning of InfTucker on subarrays and global information integration from local results. We further develop a distributed stochastic gradient descent algorithm, coupled with variational inference for model estimation. In addition, the connection between DinTucker and InfTucker is revealed in terms of model evidence. Experiments demonstrate that DinTucker maintains the predictive accuracy of InfTucker and is scalable on massive data: On multidimensional arrays with billions of elements from two real-world applications, DinTucker achieves significantly higher prediction accuracy with less training time, compared with the state-of-the-art large-scale tensor decomposition method, GigaTensor.
Fast Lasso Algorithm via Selective Coordinate Descent
Authors: Fujiwara Yasuhiro , Ida Yasutoshi , Shiokawa Hiroaki , Iwamura Sotetsu
Published: June 2022

For the AI community, the lasso proposed by Tibshirani is an important regression approach in finding explanatory predictors in high dimensional data. The coordinate descent algorithm is a standard approach to solve the lasso which iteratively updates weights of predictors in a round-robin style until convergence. However, it has high computation cost. This paper proposes Sling, a fast approach to the lasso. It achieves high efficiency by skipping unnecessary updates for the predictors whose weight is zero in the iterations. Sling can obtain high prediction accuracy with fewer predictors than the standard approach. Experiments show that Sling can enhance the efficiency and the effectiveness of the lasso.
Robustness of Bayesian Pool-Based Active Learning Against Prior Misspecification
Authors: Cuong Nguyen , Ye Nan , Lee Wee
Published: June 2022

We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all alpha-approximate algorithms are robust (i.e., near alpha-approximate) if the utility is Lipschitz continuous in the prior. We further show that robustness may not be achieved if the utility is non-Lipschitz. This suggests we should use a Lipschitz utility for AL if robustness is required. For the minimum cost setting, we can also obtain a robustness result for approximate AL algorithms. Our results imply that many commonly used AL algorithms are robust against perturbed priors. We then propose the use of a mixture prior to alleviate the problem of prior misspecification. We analyze the robustness of the uniform mixture prior and show experimentally that it performs reasonably well in practice.
Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search
Authors: Fraccaro Marco , Paquet Ulrich , Winther Ole
Published: June 2022

The Maximum Inner Product Search (MIPS) problem, prevalent in matrix factorization-based recommender systems, scales linearly with the number of objects to score. Recent work has shown that clever post-processing steps can turn the MIPS problem into a nearest neighbour one, allowing sublinear retrieval time either through Locality Sensitive Hashing or various tree structures that partition the Euclidian space. This work shows that instead of employing post-processing steps, substantially faster retrieval times can be achieved for the same accuracy when inference is not decoupled from the indexing process. By framing matrix factorization to be natively indexable, so that any solution is immediately sublinearly searchable, we use the machinery of Machine Learning to best learn such a solution. We introduce Indexable Probabilistic Matrix Factorization (IPMF) to shift the traditional post-processing complexity into the training phase of the model. Its inference procedure is based on Geodesic Monte Carlo, and adds minimal additional computational cost to standard Monte Carlo methods for matrix factorization. By coupling inference and indexing in this way, we achieve more than a 50% improvement in retrieval time against two state of the art methods, for a given level of accuracy in the recommendations of two large-scale recommender systems.