Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Privacy-Preserving Matrix Factorization via Fully Homomorphic Encryption: Extended Abstract

Recommendation systems become popular in our daily life. It is well known that the more the release of users' personal data, the better the quality of recommendation. However, such services raise serious privacy concerns for users. In this paper, focusing on matrix factorization-based recommendation systems, we propose the first privacy-preserving matrix factorization using fully homomorphic encryption. On inputs of encrypted users' ratings, our protocol performs matrix factorization over the encrypted data and returns encrypted outputs so that the recommendation system knows nothing on rating values and resulting user/item profiles. It provides a way to obfuscate the number and list of items a user rated without harming the accuracy of recommendation, and additionally protects recommender's tuning parameters for business benefit and allows the recommender to optimize the parameters for quality of service. To overcome performance degradation caused by the use of fully homomorphic encryption, we introduce a novel data structure to perform computations over encrypted vectors, which are essential operations for matrix factorization, through secure 2-party computation in part. With the data structure, the proposed protocol requires dozens of times less computation cost over those of previous works. Our experiments on a personal computer with 3.4 GHz 6-cores 64 GB RAM show that the proposed protocol runs in 1.5 minutes per iteration. It is more efficient than Nikolaenko et al.'s work proposed in CCS 2013, in which it took about 170 minutes on two servers with 1.9 GHz 16-cores 128 GB RAM.
The Effects of Mindfulness Group Training on the Rate of Obsessive-Compulsive Disorder Symptoms on the Women in Isfahan City (Iran)

Objective: To analyze and evaluate the influence of mindfulness group training on the rate of obsessive- compulsive disorder symptoms. Method and Materials: This research utilized the experimental method; consisting of a pre-test and post-test with one control group and one experimental group. The participants were 120 individuals who were clients of private & state clinical centres in Isfahan city, in Iran. In an interview, based on the revised text of the fourth diagnostic & statistical guide in mental disorders, the clients were diagnosed as suffering from obsessive- compulsive disorder. From among 120 clients, 24 people were randomly selected and were assigned to two groups: experimental and control group. The experiential group received eight sessions of treatment, that were held twice a week and each session was about two hours in length. The participants of the control group filled out the questionnaires without any instruction or treatment. Result: The results of analysis of covariance showed that the training effects of mindfulness on general symptoms of obsession in post- test and a two months follow-up were significant (P < 0.0001). In addition, the results showed that the effect of training on washing/ cleaning (P < 0.05), verification (P < 0.001), clueless (slowness) (P < 0.0001) and obsessive hesitation (P < 0.0001) symptoms in post- test & follow-up after two months was significant. Conclusion: Therefore, considering the effects, this training, with its advantages in treatment of obsessive-compulsive disorder, is suggested.
UV opacity in nearby galaxies and application to distant galaxies

The effects of dust opacity on the radiation of nearby and distant galaxies are reviewed. The geometrical distribution of the dust inside the galaxy plays a fundamental role in determining the wavelength dependence of the obscuration and the opacity of the galaxy. In the local Universe, late Hubble type galaxies appear to contain enough dust that corrections for the effect of obscuration become important. This is true especially at blue and UV wavelengths, i.e. in the wavelength range of interest for studies of massive stars and star formation processes. Multiwavelength observations provide a powerful tool for characterizing the reddening caused by dust. A ‘recipe’ is given for removing the dust reddening and recovering the UV and optical light in star-forming galaxies.
On the Influence of Obstacle Shadowing in Evaluating VANET Safety Performance

Wireless communication between vehicles enables both safety applications, such as accident avoidance, and nonsafety applications, such as traffic congestion alerts [1] with the intent of improving safety in driving conditions. Because the high costs of implementing limited test-bed environments constrain prototype testing, VANET researchers often turn instead to simulation toolsets from which a rich set of environmental scenarios are modeled. However, despite the availability of such tools, results are inconsistent. While VANET simulations allow investigators to select from a wide range of models for radio wave propagation, fading, and shadowing, they often fail to consider realistic road topologies and the presence of obstacles [2]. Safety performance assessments improve when models accurately reflect environmental conditions such as the fading effects of radio wave propagation through buildings and other obstacles. The goal of this research is to show quantitatively how accurate, deterministic obstacle fading models impact the performance assessment of VANET safety applications. An obstacle model and a fading model that uses it, along with a VANET simulation script, have been implemented and contributed to the open source ns-3 network simulation community. Results from simulation experiments that use these models show that deterministic obstacle fading greatly degrades Packet Delivery Ratio (PDR) and compares differently than stochastic fading models, such as Nakagami-m. Such networklevel results can have dramatic impacts to safety performance assessment, such as awareness range. By considering the number of packets and time tolerance window, awareness probability and communications awareness range [3] can be used to assess application reliability, thereby providing comparative insight among safety application performance. Failing to account for the effects of obstacles in safety assessment can therefore inaccurately or even greatly overstate the performance of VANET safety applications. Including realistic obstacle fading in VANET simulation modeling improves VANET assessment and strengthens safety, thus supporting one of the primary goals of connected vehicle systems. Keywords— VANET; obstacles; propagation loss; fading; shadowing; simulation; ns-3
The relationship between childhood traumas, dissociation, and impulsivity in patients with borderline personality disorder comorbid with ADHD

ABSTRACT Objective: Previous studies reported that there were significant associations between Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD). In this study, we aimed to examine complex relationship between childhood trauma, impulsivity, dissociative symptoms, and the impact of ADHD on this relationship in a sample of patients with BPD. Methods: Our samples consisted of 165 BPD patients (128 female, 37 male) and 165 healthy subjects (128 female, 37 male) of similar age and gender. We administered the semi-structured socio-demographic data form, Adult Self-Report Scale, Barratt Impulsivity Scale (BIS-11), Childhood Trauma Questionnaire, and Dissociation Questionnaire. Results: The BPD group exhibited greater ADHD symptoms, impulsivity, childhood trauma, and dissociation compared to the healthy group. A statistically significant association was found between adult ADHD symptoms and impulsivity, childhood trauma, and dissociation scores. The logistic regression analysis indicated that gender, attentional and motor impulsiveness were the predictors of ADHD in BPD patients. Among these, gender and emotional neglect were the predictors of dissociation in BPD patients. Conclusion: Our results suggested a strong association between impulsivity, childhood trauma, dissociation, and ADHD symptoms in patients with BPD. The findings are mostly consistent with the literature. Clinicians should be aware of these symptoms among these populations to develop treatment strategies.
Improving reading comprehension through Literature Circles

This research was done to test the effect of using Literature Circles to improve the reading comprehension ability of third grade (year 12) Islamic high school students from MAN Cot Gue, Aceh Besar. The objective of this study was to find out if the use of Literature Circles could improve the students’ reading comprehension more than the teacher-centered (Reading-Aloud) approach. This study used an experimental group (EG) and a control group (CG) each with 20 students. The EG was taught using Literature Circles, while the CG was taught using the teacher-centered (reading aloud) approach. The data for this research were collected by means of a pre-test and a post-test. The tests given used multiple choice questions to find out the students’ understanding of the reading narrative text used for the test. The collected data were then analyzed by using the t-test formula. The research findings showed that the mean of the post test of the EG was 58, while that of the CG was 41. The result from the t-test was 4.69 while the t-table at a level of significance with α = 0.05 was 1.697, which means that the students who were taught using Literature Circles did significantly better in reading comprehension than those who were not. In conclusion Literature Circles can be a better ways for teaching English reading comprehension for third grade (year 12) senior high school ESL students.
The Effect of Impulsiveness in Inter-Cell Interference on Throughput of TH-IR-UWB Networks

This paper considers a wireless sensor network with Time-Hopping-Impulse-Radio Ultra Wide Band as the physical layer. The network is structured as a group of interconnected cells that are not synchronized with each other. In such an unsynchronized environment, inter-cell interference is not Gaussian, but generalized Gaussian. For such a non-Gaussian random interference, its impulsiveness is determined by the Kurtosis of the distribution. This paper shows that the Kurtosis can be controlled by adjusting some practical parameters, such as pulse shape, pulse length, slot duration, number of slots per frame, number of users, etc. Further, this paper shows that network throughput can be maximized by controlling the impulsiveness. The finding has been validated through simulations.
Hardware Design of the H.264/AVC Variable Block Size Motion Estimation for Real-Time 1080HD Video Encoding

Amongst the video compression standards, the latest one is the H.264/AVC. This standard reaches the highest compression rates when compared to the previous standards. On the other hand, it has a high computational complexity. This high computational complexity makes it difficult the development of software applications running in a current processor when high definitions videos are considered. Thus, hardware implementations become essential. Addressing the hardware architectures, this work presents the architectural design for the variable block size motion estimation (VBSME) defined in the H.264/AVC standard. This architecture is based on full search motion estimation algorithm and SAD calculation. This architecture is able to produce the 41 motion vectors within a macroblock as specified in the standard. The implementation of this architecture was based on standard cell methodology in 0. 18μm CMOS technology. The architecture reached a throughput of 34 1080HD frames per second.
RPS8—a New Informative DNA Marker for Phylogeny of Babesia and Theileria Parasites in China

Piroplasmosis is a serious debilitating and sometimes fatal disease. Phylogenetic relationships within piroplasmida are complex and remain unclear. We compared the intron–exon structure and DNA sequences of the RPS8 gene from Babesia and Theileria spp. isolates in China. Similar to 18S rDNA, the 40S ribosomal protein S8 gene, RPS8, including both coding and non-coding regions is a useful and novel genetic marker for defining species boundaries and for inferring phylogenies because it tends to have little intra-specific variation but considerable inter-specific difference. However, more samples are needed to verify the usefulness of the RPS8 (coding and non-coding regions) gene as a marker for the phylogenetic position and detection of most Babesia and Theileria species, particularly for some closely related species.
SIDI: A Scalable in-Memory Density-based Index for Spatial Databases

With wide-spread use of location-based services, spatial data is becoming popular. As the data is usually huge in volume and continuously arriving to the storage in real-time, designing systems for efficiently storing this type of data is challenging. Two major issues that make building such system become complicated are the skewed distribution of data and the need of scaling the storage on multiple machines. In this paper, we propose a novel scalable in-memory density-based index for spatial databases. The key principle underlying our design is the exploitation of the stable spatial distribution of the datasets to deploy a simple but efficient index structure. We used information extracted from data in the past to split the entire space into independent pieces with similar density to ensure load-balancing and scalability. Experimental results show that the proposed solution scales well in distributed environment and outperforms common indexes in many cases.
Cross Layer based Congestion Control in Wireless Networks

In this paper, we discuss a cross layer congestion control technique of TCP Reno-2 in wireless networks. In this both TCP layer and PHY layer jointly control congestion. The PHY layer changes transmission power as per the channel condition, interference received and congestion in the network, whereas the TCP layer controls congestion using Reno-2 window based flow control. Our simulations show that the cross layer congestion control technique provides performance improvement in terms of throughput and window size variations.
Machine Flow based Energy-Power Approximation on Elastic Cloud Services

Cloud computing offers cloud mobile services with different elasticity application. The role of elasticity in cloud mobile services offers an effective mapping application on the cloud zone that matches with the resources that can be allocated with which it actually requires. Huge number of techniques have proposed in the past using energy-aware resource allocation that used heuristics method and offered with substantial amount of cloud services but failed to enhance the energy efficient management on elastic cloud computing environments. In addition to secure the Mobile Cloud Computing (MCC) guarantees the user privacy but failed to include a host trusted domain module for other cloud service providers. To attain an energy efficient system for the cloud mobile services, a method called Machine Flow based Energy-Power Approximation (MFEPA) is presented in this paper. The method MFEPA is executed for each elastic cloud services for efficient energy-power saving in the cloud mobile devices. Moreover with the design of MFEPA algorithm two objectives are attained. At first with the application of Multigrid approximation technique, the energy consumption is reduced. Next, in order to reduce the power consumption for mobile cloud services, a look-ahead control is introduced. Multiple grid (i.e.,) machines is reduced to a coarser construction, and the solution is mapped back to the inventive grid. The mapping reduces the energy during the unnecessary computing load and proves to be effective on the terminal mainframe mobile communications. The Look-ahead control in MFEPA assigns weights to all the users and decreases the power usage on the wireless interface. The power minimized up to 9.42 % averagely on different experimental results and reduces the performance degradation on the elasticity of cloud applications. MFEPA method performs the experimental work on the factors such as true positive rate, energy efficiency level, and grid mapping efficacy rate.
Brain Science and Teaching: A Forty-Year Personal History

This paper is the sharing of a record, the personal history of an educator pursuing an interest in knowledge of the brain. Over the years, this fascination sparked the idea to create a course for teachers based on brain science, with a twist. Certain course assignments would require teachers to interpret knowledge of the brain in the context of their individual classes. In 2006, I completed a dissertation entitled “The Impact of a Brain Research Course on Knowledge of the Brain and Teacher Efficacy. ” Over the past ten years, I have continued to teach brain science research to master’s level teachers. In this article, I share my observations and insights into the teaching of brain anatomy and function to teachers. The article focuses on the growing attraction of brain science research to educators. In it, I seek to illustrate how learning about the brain has created a positive force in the teaching lives of enrolled students, the learning of their students, the accommodations of curricular requirements, and the culture of the schools in which they operate. I conclude with observations regarding the utility, viability, and desirability of a course in brain research within other teacher preparation programs.
A Network Control Theory Approach to Modeling and Optimal Control of Zoonoses: Case Study of Brucellosis Transmission in Sub-Saharan Africa

Background Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. Methodology/Principal Findings A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. Conclusions/Significance The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases.
Blood Glucose Levels and Genetic Factors as Predictors of Neurocognitive Outcomes

Alzheimer’s disease (AD), a neurodegenerative disease characterized by progressive cognitive decline, is becoming increasingly prevalent as the population ages. There is no effective treatment for AD, so manipulating modifiable risk factors before clinical symptoms of dementia develop may be the most effective course to prevent, delay, or modify the course of AD. Type II diabetes mellitus (DM), characterized by hyperglycemia and insulin resistance, affects over a quarter of older adults and has been linked with AD, cognitive decline, and brain atrophy. Because DM is preventable and treatable, it represents an intervention target for AD. This dissertation investigated the association between glucose levels and three outcomes – performance on a battery of cognitive tests over time, time to symptom onset of mild cognitive impairment (MCI) or dementia, and cortical thickness in ADsignature regions (i.e., entorhinal cortex, inferior temporal gyrus, parahippocampal gyrus pars triangularis, precuneus, superior frontal and parietal gyri, supramarginal gyrus, and temporal pole). Additionally, it investigated how two genes – apolipoprotein E (APOE) and translocase of the outer mitochondrial membrane (TOMM) 40 – may modify the relationship between glucose and these outcomes. Both the APOE ε4 allele and the TOMM40 very long/very long (VL/VL) genotype (versus the short/short (S/S) genotype) have been associated with increased risk of dementia, cognitive decline, and brain atrophy, and some evidence suggests that these genetic factors may further increase the risk in individuals with DM. This research was conducted using data from the BIOCARD Study (n=349), which
Large-Scale Newscast Computing on the Internet

This paper introduces the newscast model of computation for large-scale computing on the Internet. The engine realizing this model is a lazy fully distributed information propagation protocol among the participants which is responsible for membership management and communication. It maintains a constantly changing communication graph over the participants. This graph has useful emergent properties like small diameter and sufficiently random structure without deploying special purpose protocols to achieve these properties. For adding a new participant only the address of an arbitrary member is needed and for removal no action is necessary. We provide theoretical and empirical evidence that—besides being simple and lightweight—our newscast computing engine is extremely scalable and robust. We also suggest some interesting application areas including information dissemination, monitoring of large systems, resource sharing and efficient multicasting.
Monitoring Collective Attention During Disasters

The proliferation of vast information shared through social media and other communication technologies has led us to an era of attention scarcity. Effective crisis response requires both a systematic understanding of how collective attention emerges during disaster events and robust techniques for monitoring the public's attention shift due to the events.However, "attention" is an abstract concept that is very challenging to characterize. In this work, we propose an attention shift network to systematically analyze the dynamics of collective attention in response to real-world exogenous shocks such as disasters. Using hashtags appeared in Twitter users' complete timelines around a violent terrorist attack -- the Paris attacks occurred on November 13, 2015 -- we thoroughly investigate the properties of network structures and reveal the temporal dynamics of the collective attention under both regular time and exogenous shock.