Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Compulsive Internet Use: It's All about Sex!

The objective of this research was to assess the predictive power of various Internet applications on the development of compulsive Internet use (CIU). The study has a two-wave longitudinal design with an interval of 1 year. The first measurement contained 447 adult heavy Internet users who used the Internet at least 16 h per week and had Internet access at home for at least 1 year. For the second measurement, all participants were invited again, of whom 229 responded. By means of an online questionnaire, the respondents were asked about the time spent on various Internet applications and CIU. On a cross-sectional basis, gaming and erotica seem the most important Internet applications related to CIU. On a longitudinal basis, spending a lot of time on erotica predicted an increase in CIU 1 year later. The addictive potential of the different applications varies; erotica appears to have the highest potential.
Social motion : mobile networking through sensing human behavior

Low-level sensors can provide surprisingly high-level information about social interactions. The goal of this thesis is to define the components of a framework for sensing social context with mobile devices. We describe several sensing technologies including infrared transceivers, radio frequency scanners, and accelerometers that both capture social signals and meet the design constraints of mobile devices. Through the analysis of several large datasets, we identify features from these sensors that correlate well with the underlying social structure of interacting groups of people. We then detail the work that we have done creating infrastructure that integrates social sensors into social applications that run on mobile devices. Thesis Supervisor: Alex (Sandy) Pentland Title: Toshiba Professor of Media Arts and Sciences
Generating Object Segmentation Proposals Using Global and Local Search

We present a method for generating object segmentation proposals from groups of superpixels. The goal is to propose accurate segmentations for all objects of an image. The proposed object hypotheses can be used as input to object detection systems and thereby improve efficiency by replacing exhaustive search. The segmentations are generated in a class-independent manner and therefore the computational cost of the approach is independent of the number of object classes. Our approach combines both global and local search in the space of sets of superpixels. The local search is implemented by greedily merging adjacent pairs of superpixels to build a bottom-up segmentation hierarchy. The regions from such a hierarchy directly provide a part of our region proposals. The global search provides the other part by performing a set of graph cut segmentations on a superpixel graph obtained from an intermediate level of the hierarchy. The parameters of the graph cut problems are learnt in such a manner that they provide complementary sets of regions. Experiments with Pascal VOC images show that we reach state-of-the-art with greatly reduced computational cost.
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection

Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases under latency constraints. Cloud services serve up to thousands of concurrent requests per second and can adjust critical parameters by leveraging performance metrics. In this work, we use continuous deep reinforcement learning to learn optimal cache expirations for HTTP caching in content delivery networks. To this end, we introduce a technique for asynchronous experience management called delayed experience injection, which facilitates delayed reward and next-state computation in concurrent environments where measurements are not immediately available. Evaluation results show that our approach based on normalized advantage functions and asynchronous CPU-only training outperforms a statistical estimator.
Effects of Chronic Renal Failure on Kidney Drug Transporters and Cytochrome P450 in Rats

Chronic renal failure (CRF) leads to decreased drug renal clearance due to a reduction in the glomerular filtration rate. However, little is known about how renal failure affects renal metabolism and elimination of drugs. Because both depend on the activity of uptake and efflux by renal transporters as well as enzymes in tubular cells, the purpose of this study was to investigate the effects of CRF on the expression and activity of select renal drug transporters and cytochrome P450. Two groups of rats were studied: control and CRF (induced by 5/6 nephrectomy). Compared with control rats, we observed reductions in the expression of both protein and mRNA of Cyp1a, sodium-dependent phosphate transport protein 1, organic anion transporter (Oat)1, 2, and 3, OatK1/K2, organic anion-transporting polypeptide (Oatp)1 and 4c1, P-glycoprotein, and urate transporter 1, whereas an induction in the protein and mRNA expression of Mrp2, 3, and 4 and Oatp2 and 3 was observed. Cyp3a expression remained unchanged. Similar results were obtained by incubating a human proximal tubule cell line (human kidney-2) with sera from CRF rats, suggesting the presence of uremic modulators. Finally, the renal elimination of [3H]digoxin and [14C]benzylpenicillin was decreased in CRF rats, compared with controls, as shown by a 4- and 9-fold accumulation, respectively, of these drugs in kidneys of rats in CRF. Our results demonstrate that CRF affects the expression and activity of several kidney drug transporters leading to the intrarenal accumulation of drugs and reduced renal clearance that could, at least partially, explain the tubular toxicity of many drugs.
Leakage-Resilient Authentication and Encryption from Symmetric Cryptographic Primitives

Leakage-resilient cryptosystems aim to maintain security in situations where their implementation leaks physical information about their internal secrets. Because of their efficiency and usability on a wide range of platforms, solutions based on symmetric primitives (such as block ciphers) are particularly attractive in this context. So far, the literature has mostly focused on the design of leakage-resilient pseudorandom objects (e.g. PRGs, PRFs, PRPs). In this paper, we consider the complementary and practically important problem of designing secure authentication and encryption schemes. For this purpose, we follow a pragmatic approach based on the advantages and limitations of existing leakage-resilient pseudorandom objects, and rely on the (arguably necessary, yet minimal) use of a leak-free component. The latter can typically be instantiated with a block cipher implementation protected by traditional countermeasures, and we investigate how to combine it with the more intensive use of a much more efficient (less protected) block cipher implementation. Based on these premises, we propose and analyse new constructions of leakage-resilient MAC and encryption schemes, which allow fixing security and efficiency drawbacks of previous proposals in this direction. For encryption, we additionally provide a detailed discussion of why previously proposed (indistinguishability based) security definitions cannot capture actual side-channel attacks, and suggest a relaxed and more realistic way to quantify leakage-resilience in this case, by reducing the security of many iterations of the primitive to the security of a single iteration, independent of the security notion guaranteed by this single iteration (that remains hard to define).
Causal Inference and Uplift Modelling: A Review of the Literature

Uplift modeling refers to the set of techniques used to model the incremental impact of an action or treatment on a customer outcome. Uplift modeling is therefore both a Causal Inference problem and a Machine Learning one. The literature on uplift is split into 3 main approaches–the Two-Model approach, the Class Transformation approach and modeling uplift directly. Unfortunately, in the absence of a common framework of causal inference and notation, it can be quite di cult to assess those three methods. In this paper, we use the Rubin (1974) model of causal inference and its modern “econometrics” notation to provide a clear comparison of the three approaches and generalize one of them. To our knowledge, this is the first paper that provides a unified review of the uplift literature. Moreover, our paper contributes to the literature by showing that, in the limit, minimizing the Mean Square Error (MSE) formula with respect to a causal e↵ect estimator is equivalent to minimizing the MSE in which the unobserved treatment e↵ect is replaced by a modified target variable. Finally, we hope that our paper will be of use to researchers interested in applying Machine Learning techniques to causal inference problems in a business context as well as in other fields: medicine, sociology or economics.
Cost‐Effectiveness of Pediatric Heart Transplantation Across a Positive Crossmatch for High Waitlist Urgency Candidates

Allosensitized children listed with a requirement for a negative prospective crossmatch have high mortality. Previously, we found that listing with the intent to accept the first suitable organ offer, regardless of the possibility of a positive crossmatch (TAKE strategy), results in a survival advantage from the time of listing compared to awaiting transplantation across a negative crossmatch (WAIT). The cost‐effectiveness of these strategies is unknown. We used Markov modeling to compare cost‐effectiveness between these waitlist strategies for allosensitized children listed urgently for heart transplantation. We used registry data to estimate costs and waitlist/posttransplant outcomes. We assumed patients remained in hospital after listing, no positive crossmatches for WAIT, and a base‐case probability of a positive crossmatch of 47% for TAKE. Accepting the first suitable organ offer cost less ($405 904 vs. $534 035) and gained more quality‐adjusted life years (3.71 vs. 2.79). In sensitivity analyses, including substitution of waitlist data from children with unacceptable antigens specified during listing, TAKE remained cost‐saving or cost‐effective. Our findings suggest acceptance of the first suitable organ offer for urgently listed allosensitized pediatric heart transplant candidates is cost‐effective and transplantation should not be denied because of allosensitization status alone.
An Analytic Center Cutting Plane Approach for Conic Programming

We analyze the problem of finding a point strictly interior to a bounded, convex, and fully dimensional set from a finite dimensional Hilbert space. We generalize the results obtained for the linear programming (LP), semidefinite programming (SDP), and second-order core programming (SOCP) cases. The cuts added by our algorithm are central and conic. In our analysis, we find an upper bound for the number of Newton steps required to compute an approximate analytic center. Also, we provide an upper bound for the total number of cuts added to solve the problem. This bound depends on the quality of the cuts, the dimensionality of the problem and the thickness of the set we are considering.
MiR‐135b is a direct PAX6 target and specifies human neuroectoderm by inhibiting TGF‐β/BMP signaling

Several transcription factors (TFs) have been implicated in neuroectoderm (NE) development, and recently, the TF PAX6 was shown to be critical for human NE specification. However, microRNA networks regulating human NE development have been poorly documented. We hypothesized that microRNAs activated by PAX6 should promote NE development. Using a genomics approach, we identified PAX6 binding sites and active enhancers genome‐wide in an in vitro model of human NE development that was based on neural differentiation of human embryonic stem cells (hESC). PAX6 binding to active enhancers was found in the proximity of several microRNAs, including hsa‐miR‐135b. MiR‐135b was activated during NE development, and ectopic expression of miR‐135b in hESC promoted differentiation toward NE. MiR‐135b promotes neural conversion by targeting components of the TGF‐β and BMP signaling pathways, thereby inhibiting differentiation into alternate developmental lineages. Our results demonstrate a novel TF‐miRNA module that is activated during human neuroectoderm development and promotes the irreversible fate specification of human pluripotent cells toward the neural lineage.
How practitioners perceive the relevance of software engineering research

The number of software engineering research papers over the last few years has grown significantly. An important question here is: how relevant is software engineering research to practitioners in the field? To address this question, we conducted a survey at Microsoft where we invited 3,000 industry practitioners to rate the relevance of research ideas contained in 571 ICSE, ESEC/FSE and FSE papers that were published over a five year period. We received 17,913 ratings by 512 practitioners who labelled ideas as essential, worthwhile, unimportant, or unwise. The results from the survey suggest that practitioners are positive towards studies done by the software engineering research community: 71% of all ratings were essential or worthwhile. We found no correlation between the citation counts and the relevance scores of the papers. Through a qualitative analysis of free text responses, we identify several reasons why practitioners considered certain research ideas to be unwise. The survey approach described in this paper is lightweight: on average, a participant spent only 22.5 minutes to respond to the survey. At the same time, the results can provide useful insight to conference organizers, authors, and participating practitioners.
Source of the Ice-Binding Specificity of Antifreeze Protein Type I

Antifreeze proteins (AFPs) are a group of structurally very diverse proteins with the unique capability of binding to the surface of seed ice crystals and inhibiting ice crystal growth. The AFPs bind with high affinity to specific planes of the ice crystal. Previously, this affinity of AFPs has been ascribed to the formation of multiple hydrogen bonds across the protein-ice interface, but more recently van der Waals interactions have been suggested to be the dominant energetic factors for the adsorption. To determine whether van der Waals interactions are also responsible for the binding specificities of AFPs, the protein-ice interaction of the helical AFP Type I from winter flounder (HPLC6) was studied using a Monte Carlo rigid body docking approach. HPLC6 binds in the [1102] direction of the [2021] plane, with the Thr-Ala-Asn surface comprising the protein's binding face. The binding of HPLC6 to this ice plane is highly preferred, but the protein is also found to bind favorably to the [1010] prism plane using a different protein surface comprised of Thr and Ala residues. The results show that van der Waals interactions, despite accounting for most of the intermolecular energy (>80%), are not sufficient to completely explain the AFP binding specificity.
Self-Organization of Shortest Spanning Tree and Optimal Sink Node Position for Various Shapes of Large-Scale Wireless Sensor Networks

The primary challenges with wireless sensor networks are how to construct the shortest spanning tree and how to determine the optimal sink node position in terms of minimizing the data transmission times and their variances for gathering data from all sensor nodes to a sink node. To solve these two problems, we previously proposed a basic algorithm that uses the polygonal affine shortening algorithm with flow aggregation in a fully distributed manner. In this paper, we proposed an algorithm that is more than 25% faster than the previously proposed algorithm and also proves that the proposed algorithm works in wireless sensor networks. The proposed algorithm was implemented on a well-known multi-agent simulator called Net logo.
AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks

In this paper, we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the nature-inspired ant colony optimisation framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. Copyright © 2005 AEIT.
Anxiety and Loneliness in the Iranian Older Adults

Aging is a delicate part of our life and taking care of the subsequent problems and needs is a major social necessity. This study aims at determining the relationship between anxiety and loneliness among the Iranian older adults. In this correlational study, 200 older adults in Qom, Iran, were selected through the convenience sampling method. To gather the data, the Geriatric Anxiety Scale (GAS) and the UCLA Loneliness Scale (version 3) were used as well as a questionnaire containing demographic variables. The sample consisted of 66% male, 91.5% of whom were married and aged from 60 to 93 (Mean: 68.32, SD: 7.1). Analytic findings showed that the GAS total score and three subscales were strongly correlated with the UCLA-LS score. Loss of interpersonal relationship leads to loneliness, fear of losing and anxiety due to separation at the end. According to the results and due to the high level of loneliness of the elderly in this study, it is suggested that through increasing social relationship and strengthening the interactions in the elderly, one can replace a new interpersonal relation with the lost one to decrease the feeling of loneliness. Therefore it could be a great assistance to reduce the anxiety of separation in the elderly.
Computing shortest paths among curved obstacles in the plane

In this paper, we study the problem of finding Euclidean shortest paths among curved obstacles in the plane. We model curved obstacles as splinegons. A splinegon can be viewed as replacing each edge of a polygon by a convex curved edge, and each curved edge is assumed to be of O(1) complexity. Given in the plane two points s and t and a set of h pairwise disjoint splinegons with a total of n vertices, we present an algorithm that can compute a shortest path from s to t avoiding the splinegons in O(n+hlogεh+k) time for any ε>0, where k is a parameter sensitive to the input splinegons and k=O(h2). If all splinegons are convex, a common tangent of two splinegons is "free" if it does not intersect the interior of any splingegon; our techniques yield an output sensitive algorithm for computing all free common tangents of the h splinegons in O(n+hlogh+k) time and O(n) working space, where k is the number of all free common tangents.
On the design and development of a rough terrain robot for rescue missions

Rescue Robots play an important role during rescue missions in disasters such as 9/11, which caused more than 2,000 deaths and thousands of injuries. However, tele-operating rescue robots are unable to perform their tasks constantly due to the limitation of current wireless communication technology. Therefore, rescue robots with the capability of performing their tasks autonomously during temporarily lost connections to the control base would be ideal. This paper introduces our development of a semi-autonomous rough terrain robot for rescue missions. The robot's hardware components, system architecture, and software architecture are described in order to provide a general overview of our robot. An alternative and comprehensive map-generating algorithm is presented and discussed. Finally, experimental setup and results from a testing arena are reported.
Monotone Classification with Decision Trees

In machine learning, monotone classification is concerned with a classification function to learn in order to guarantee a kind of monotonicity of the class with respect to attribute values. In this paper, we focus on rank discrimination measures to be used in decision tree induction, i.e., functions able to measure the discrimination power of an attribute with respect to the class taking into account the monotonicity of the class with respect to the attribute. Three new measures are studied in detail and an experimental analysis is also provided, comparing the proposed approach with other well-known monotone and non-monotone classifiers in terms of classification accuracy.
Gold Nanoparticles Surface Plasmon Resonance Enhanced Signal for the Detection of Small Molecules on Split-Aptamer Microarrays (Small Molecules Detection from Split-Aptamers)

The detection of small molecules by biosensors remains a challenge for diagnostics in many areas like pharmacology, environment or homeland security. The main difficulty comes from both the low molecular weight and low concentrations of most targets, which generally requires an indirect detection with an amplification or a sandwich procedure. In this study, we combine both strategies as the amplification of Surface Plasmon Resonance imaging (SPRi) signal is obtained by the use of gold nanoparticles and the sequence engineering of split-aptamers, short oligonucleotides strands with strong affinity towards small targets, allows for a sandwich structure. Combining those two strategies, we obtained state-of-the-art results in the limit of detection (LOD = 50 nM) with the model target adenosine. Furthermore, the SPRi detection led on aptamer microarrays paves the way for potential multi-target detections thanks to the multi-probe imaging approach.
Set Stability and Controllability for Switched Non-homogeneous Linear Systems

In this paper, we consider some control problems of switched non-homogeneous linear systems. First, the set stability of switched non-homogeneous linear systems is considered. It is proved that as the linear parts have a common quadratic Lyapunov function, the set stability is assured. The problem of searching smallest attracting set is then considered. Then we assume the switching law is controllable and investigate the controllability condition within the attracting region. Finally, we consider the aggregation and the control of aggregation of flocking behavior. Particularly, the results obtained are implemented to analyzing and manipulating of a group of mobile robots.