Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Augmented Reality Practical on Mobile Phones, Part 1

In 2003, we started an AR framework for mobile phones. We intended its first generation as primarily a proof of feasibility. The second generation was an attempt to port a fully featured PC-based AR framework, Studierstube 4, to a phone platform. You can port existing applications and make them run on mobile phones. However, as we had to painfully experience ourselves, this approach typically produces slow, bloated, and unstable software. Optimally using phones' scarce resources requires different algorithms and architectural decisions than for PCs, leading to a complete reengineering of an existing solution. So, for the third generation, Studierstube ES, we largely abandoned compatibility requirements and added new elements to the design, such as an asymmetric client-server technique, that are specific to mobile devices. In this first installment of our two-part tale of Studierstube ES and what we've learned along the way, we describe the mobile phone platform's restrictions and how our software architecture allows fast development of mobile phone AR applications.
Mini-Tn5 transposon derivatives for insertion mutagenesis, promoter probing, and chromosomal insertion of cloned DNA in gram-negative eubacteria

A collection of Tn5-derived minitransposons has been constructed that simplifies substantially the generation of insertion mutants, in vivo fusions with reporter genes, and the introduction of foreign DNA fragments into the chromosome of a variety of gram-negative bacteria, including the enteric bacteria and typical soil bacteria like Pseudomonas species. The minitransposons consist of genes specifying resistance to kanamycin, chloramphenicol, streptomycin-spectinomycin, and tetracycline as selection markers and a unique NotI cloning site flanked by 19-base-pair terminal repeat sequences of Tn5. Further derivatives also contain lacZ, phoA, luxAB, or xylE genes devoid of their native promoters located next to the terminal repeats in an orientation that affords the generation of gene-operon fusions. The transposons are located on a R6K-based suicide delivery plasmid that provides the IS50R transposase tnp gene in cis but external to the mobile element and whose conjugal transfer to recipients is mediated by RP4 mobilization functions in the donor.
5.10 A 4.7MHz 53μW fully differential CMOS reference clock oscillator with −22dB worst-case PSNR for miniaturized SoCs

Low-power CMOS reference clock oscillators have been widely used in miniaturized SoCs for emerging microsystems such as implantable biomedical devices and smart sensors. In such SoCs, as the supply voltage shrinks and the level of analog and digital circuit integration increases to meet rigorous power and area constraints, the noise from other blocks (especially digital blocks) couples through supply and ground lines and poses a serious threat to the performance of CMOS reference clock oscillators.
Linear view synthesis using a dimensionality gap light field prior

Acquiring and representing the 4D space of rays in the world (the light field) is important for many computer vision and graphics applications. Yet, light field acquisition is costly due to their high dimensionality. Existing approaches either capture the 4D space explicitly, or involve an error-sensitive depth estimation process. This paper argues that the fundamental difference between different acquisition and rendering techniques is a difference between prior assumptions on the light field. We use the previously reported dimensionality gap in the 4D light field spectrum to propose a new light field prior. The new prior is a Gaussian assigning a non-zero variance mostly to a 3D subset of entries. Since there is only a low-dimensional subset of entries with non-zero variance, we can reduce the complexity of the acquisition process and render the 4D light field from 3D measurement sets. Moreover, the Gaussian nature of the prior leads to linear and depth invariant reconstruction algorithms. We use the new prior to render the 4D light field from a 3D focal stack sequence and to interpolate sparse directional samples and aliased spatial measurements. In all cases the algorithm reduces to a simple spatially invariant deconvolution which does not involve depth estimation.
Machine Learning the Harness Track: A Temporal Investigation of Race History on Prediction

Machine learning techniques have shown their usefulness in accurately predicting greyhound races. Many of the studies within this domain focus on two things; win-only wagers and using a very particular combination of race history. Our study investigates altering these properties and studying the results. In particular we found a race history combination that optimizes our S&C Racing system’s predictions on seven different wager types. From this, S&C Racing posted an impressive 50.44% accuracy in selecting winning wagers with a payout of $609.34 and a betting return of $10.06 per dollar wagered
Secondary/tertiary benzenesulfonamides with inhibitory action against the cytosolic human carbonic anhydrase isoforms I and II

Carbonic anhydrase inhibitors of primary sulfonamide type, RSO2NH2, have clinical applications as diuretics, antiglaucoma, antiepileptic, antiobesity and antitumor drugs. Here we investigated inhibition of two human cytosolic isozymes, hCA I and II, with a series of secondary/tertiary sulfonamides, incorporating tosyl moieties (CH3C6H4SO2NR1R2). Most compounds inhibited both isoforms in low micromolar range, with inhibition constants between 0.181–6.01 μM against hCA I, and 0.209–0.779 μM against hCA II, respectively. These findings point out that substituted benzenesulfonamides may be used as leads for generating interesting CAIs probably possessing a distinct mechanism of action compared to primary sulfonamides. Indeed, classical RSO2NH2 inhibitors bind in deprotonated form to the Zn(II) ion from the CA active site and participate in many other favorable interactions with amino acid residues lining the cavity. The secondary/tertiary sulfonamides cannot bind to the zinc due to steric hindrance and probably are accommodated at the entrance of the active site, in coumarin binding-site.
How to identify specialized research communities related to a researcher's changing interests

Scholarly events and venues are increasing rapidly in number. This poses a challenge for researchers who seek to identify events and venues related to their work in order to draw more efficiently and comprehensively from published research and to share their own findings more effectively. Such efforts are hampered also by the fact that no rating system yet exists to assist researchers in culling the venues most relevant to their current readings and interests. This study describes a methodology we developed in response to this need, one that recommends scholarly venues related to researchers' specific interests according to personalized social web indicators. Our experiments applying our proposed rating and recommendation method show that it outperforms the baseline venue recommendations in terms of accuracy and ranking quality.
Fighting Evasive Malware with DVasion

Title of thesis: FIGHTING EVASIVE MALWARE WITH DVASION Matthew Ryan Gilboy, Master of Science, 2016 Thesis directed by: Professor Rajeev Barua Department of Electrical and Computer Engineering Malware is a foundational component of cyber crime that enables an attacker to modify the normal operation of a computer or access sensitive, digital information. Despite the extensive research performed to identify such programs, existing schemes fail to detect evasive malware, an increasingly popular class of malware that can alter its behavior at run-time, making it difficult to detect using today’s state of the art malware analysis systems. In this thesis, we present DVasion, a comprehensive strategy that exposes such evasive behavior through a multi-execution technique. DVasion successfully detects behavior that would have been missed by traditional, single-execution approaches, while addressing the limitations of previously proposed multi-execution systems. We demonstrate the accuracy of our system through strong parallels with existing work on evasive malware, as well as uncover the hidden behavior within 167 of 1,000 samples. FIGHTING EVASIVE MALWARE WITH DVASION
A Probabilistic Memetic Framework

Memetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms, or genetic local searches. In the last decade, MAs have been demonstrated to converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of real-world problems. Despite the success and surge in interests on MAs, many of the successful MAs reported have been crafted to suit problems in very specific domains. Given the restricted theoretical knowledge available in the field of MAs and the limited progress made on formal MA frameworks, we present a novel probabilistic memetic framework that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum. Further, the framework balances evolution and individual learning by governing the learning intensity of each individual according to the theoretical upper bound derived while the search progresses. Theoretical and empirical studies on representative benchmark problems commonly used in the literature are presented to demonstrate the characteristics and efficacies of the probabilistic memetic framework. Further, comparisons to recent state-of-the-art evolutionary algorithms, memetic algorithms, and hybrid evolutionary-local search demonstrate that the proposed framework yields robust and improved search performance.
Travel-time prediction with deep learning

Travel time prediction is a challenging problem in Intelligent Transportation Systems (ITS). Accurate travel time information helps motorists plan their routes more wisely. This, in turn, alleviates traffic congestion and improves operation efficiency. A number of travel time prediction techniques exist; however, most of them are based on shallow learning architectures. In contrast to deep learning architectures, shallow learning architectures are lack of features-learning capability. In this paper, we propose an effective travel time prediction technique based on a concept of Deep Belief Networks (DBN). In our method, a stack of Restricted Boltzmann Machines (RBM) is used to automatically learn generic traffic features in an unsupervised fashion, and then a sigmoid regression is used to predict travel time in a supervised fashion. The experimental results, based on real traffic data, show that the proposed method can achieve great performance in terms of prediction accuracy.
Fusion and Filtering in Distributed Intrusion Detection Systems

False alarms and timely identification of new attacks are two of the biggest challenges to the effective use of network intrusion detection systems (NIDS). A potential means for addressing these shortcomings in modern NIDS is employing multiple, distributed network intrusion detection systems (DNIDS). In this paper we consider the potential benefits of DNIDS by addressing two open problems. The first problem is how to combine data from multiple intrusion sensors in a network. This is known as the fusion problem. The second problem is how to identify the most important data provided by multiple sensors in a network. This is known as the filtering problem. We develop a series of analytic and simulation models to assess the potential benefits of DNIDS for reducing false alarms and improving timeliness of detection for different fusion and filtering strategies. Our analysis explores the trade-offs when fusion and filtering are used together and shows that significant improvements are possible.
Location Based Agricultural Product Recommendation System Using Novel KNN Algorithm

It is evident that the need for personalized product recommendation is much needed these days. Generally, product recommender systems are implemented in web servers that make use of data, implicitly obtained as results of the collection of Web browsing patterns of the users. Here, the project's motive is to provide location-based agricultural product recommendation system using a novel KNN algorithm by ensuring effective communication and transparency in agriculture trade marketing among buyers and sellers (farmers). It helps the farmer to fix up the market price by preventing the rue pricing of their products. The farmer can post their products into the application with price and other details like a timestamp of harvesting, color, size, the absence of pest, freshness, ripeness etc. Based on the location, the distance between the seller and buyer is calculated using great circle distance. An improved Novel KNN algorithm is used to find the K Nearest Seller by calculating the distance between the sellers and buyer using a Euclidean distance metric. The details posted by the farmers and buyers are stored and updated in a database dynamically. The recommender system recommends nearest sellers and their agricultural products based on buyer interest. The performance of the system is analyzed in terms of accuracy and mean absolute error.
Characterization of 80 kDa Human Sperm Antigen Responsible for Immunoinfertility

PROBLEM: An 80 kDa human sperm antigen (80 kDa HSA) has been identified by western blot technique using serum of an immunoinfertile woman as a probe. The 80 kDa HSA has been subsequently purified from sperm extract and investigated for antifertility effects. METHOD OF STUDY: The purified 80 kDa HSA was used to immunize adult male and female rats. Rabbit anti 80 kDa HSA antibodies were used for immunofluorescent and immunohistochemical staining to demonstrate the presence of 80 kDa HSA on the sperm and to investigate its tissue distribution. The N‐terminal sequence of native 80 kDa HSA and the peptides obtained by its endoproteinase Lys‐C was determined. RESULTS: Active immunization of male and female rats with 80 kDa HSA caused infertility in all the immunized animals. Immunofluorescent staining showed its localization on the head region of the human and rat spermatozoa. While immunohistochemical studies showed its localization in the testes and epididymis but not in other somatic tissues. Partial amino‐acid sequence analysis showed no sequence homology with any of the known protein in the database. CONCLUSIONS: 80 kDa HSA is a promising candidate antigen for immunocontraception as it is characterized and found to cause infertility upon active immunization, specific to spermatozoa and is conserved.
Improvement of operation efficiency in Winnipeg Regional Health Authority (WRHA) hemodialysis (HD) units using discrete-event simulation modeling

Facility based Hemodialysis (HD) treats over 75% of Canadian patients with kidney failure. HD in Winnipeg costs approximately $60,000 per patient annually and predictions indicate this population will double within the next 10 years. This research investigates workflow bottlenecks for improvement in HD units of Winnipeg Health Sciences Centre. A validated discrete-event simulation (DES) model is built as an efficient decision-making tool to evaluate different strategies in HD care processes for improvement based on proposed alternatives for renal program workflows. Simulation modeling is used to study the behaviour change of entities and resources in HD systems. Based on data of 2122 patient visits, proposed alternative scenarios in the simulation model examine key performance indicators (KPIs) including wait-time to start dialysis machines, length of stay, and percentage of times that dialysis machines start within the target time. The application of proposed improvement strategies results in desirable outcomes on KPIs up-to 31%. The solution can be applied to support future HD units’ improvements.
Robust Message-Privacy Preserving Image Copy Detection for Cloud-based Systems

In this paper we propose an architecture for message-privacy preserving copy detection and content identification for images based on the signs of the Discrete Cosine Transform (DCT) coefficients. The architecture allows for searching in encrypted data and places the computational burden on the server. Sign components of the low frequency DCT coefficients of an image are used to generate a dual set of keys that in turn are used to encrypt the source image and serve as a robust hash that can be queried for content identification. The statistical properties of these DCT sign vectors are modelled and we investigate the influence of a set of real world image distortions on them. Finally, the trade-off between the discriminative power of such vectors, the offered security and the resilience against errors is demonstrated.
A Method to Model the Maximum Power Output of Photovoltaic Modules Using Statistical Analysis and Matlab-Simulink Simulation

A photovoltaic module has been fabricated with 36 pseudo-square large size mono-crystalline silicon solar cells (125mm X 125mm) with the spacing between cells kept as 3mm and 2mm in horizontal and vertical directions respectively and its illuminated current-voltage (I-V) characteristics have been measured using a solar simulator. All the cells used in the modules were earlier modeled using the two-diode equivalent circuit model of the solar cell; and the parameters of all the solar cells were individually extracted using the Particle Swarm Optimization (PSO) method through curve-fitting of measured and calculated I-V curves. A Matlab-Simulink model has been developed for the module to simulate the characteristics of the fabricated modules. Simulated I-V curve has been matched to the measured I-V curve of a 36-cell series connected module by adjustment of various parameters and environment factors. Also, output of one random 36-cells module has been simulated 2500 times with parameters of the individual cells randomly picked through the lognormal distribution and the maximum output power from the module has been statistically analyzed. Keywords—PV Modules; Matlab-Simulink Model; Module Characteristics; Simulated Module Characteristics; PV System; Maximum Power Output
Identification of a fourth family of lycopene cyclases in photosynthetic bacteria

A fourth and large family of lycopene cyclases was identified in photosynthetic prokaryotes. The first member of this family, encoded by the cruA gene of the green sulfur bacterium Chlorobium tepidum, was identified in a complementation assay with a lycopene-producing strain of Escherichia coli. Orthologs of cruA are found in all available green sulfur bacterial genomes and in all cyanobacterial genomes that lack genes encoding CrtL- or CrtY-type lycopene cyclases. The cyanobacterium Synechococcus sp. PCC 7002 has two homologs of CruA, denoted CruA and CruP, and both were shown to have lycopene cyclase activity. Although all characterized lycopene cyclases in plants are CrtL-type proteins, genes orthologous to cruP also occur in plant genomes. The CruA- and CruP-type carotenoid cyclases are members of the FixC dehydrogenase superfamily and are distantly related to CrtL- and CrtY-type lycopene cyclases. Identification of these cyclases fills a major gap in the carotenoid biosynthetic pathways of green sulfur bacteria and cyanobacteria.
Feral Pigeons (Columba livia) Prefer Genetically Similar Mates despite Inbreeding Depression

Avoidance of mating between related individuals is usually considered adaptive because it decreases the probability of inbreeding depression in offspring. However, mating between related partners can be adaptive if outbreeding depression is stronger than inbreeding depression or if females gain inclusive fitness benefits by mating with close kin. In the present study, we used microsatellite data to infer the parentage of juveniles born in a French colony of feral pigeons, which allowed us to deduce parent pairs. Despite detectable inbreeding depression, we found that pairwise relatedness between mates was significantly higher than between nonmates, with a mean coefficient of relatedness between mates of 0.065, approximately half the theoretical value for first cousins. This higher relatedness between mates cannot be explained by spatial genetic structure in this colonial bird; it therefore probably results from an active choice. As inbreeding but not outbreeding depression is observed in the study population, this finding accords with the idea that mating with genetically similar mates can confer a benefit in terms of inclusive fitness. Our results and published evidence suggest that preference for related individuals as mates might be relatively frequent in birds.
Large scale style based dating of medieval manuscripts

In this paper we propose a novel approach for manuscript dating based on shape statistics. Our goal was to develop a strategy well suited for a large scale dating effort where heterogeneous collections of thousands of manuscripts could be automatically processed. The proposed method takes the gray scale image as input, then uses the stroke width transform and a statistical model of the gradient image to find ink boundaries. Finally, a distribution over common shapes, quantified using shape context descriptors, is produced for each manuscript image. The proposed method is binarization-free, rotational invariant and requires minimal segmentation. We evaluate our work on the 10000+ manuscripts collection "Svenskt diplomatariums huvudkartotek", consisting of charters from the medieval period of todays Sweden. The images, originally intended for web viewing, were of low quality and had compression artifacts. Due to unsupervised feature learning and regression, the collection could be dated with a median absolute error below 19 years even though we only used 5% of the labels in the estimator training.
Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease

Both normal aging and Alzheimer's disease (AD) have been associated with a reduction in functional brain connectivity. It is unknown how connectivity patterns due to aging and AD compare. Here, we investigate functional brain connectivity in 12 young adults (mean age 22.8 ± 2.8), 12 older adults (mean age 73.1 ± 5.2) and 12 AD patients (mean age 74.0 ± 5.2; mean MMSE 22.3 ± 2.5). Participants were scanned during 6 different sessions with resting state functional magnetic resonance imaging (RS-fMRI), resulting in 72 scans per group. Voxelwise connectivity with 10 functional networks was compared between groups (p < 0.05, corrected). Normal aging was characterized by widespread decreases in connectivity with multiple brain networks, whereas AD only affected connectivity between the default mode network (DMN) and precuneus. The preponderance of effects was associated with regional gray matter volume. Our findings indicate that aging has a major effect on functional brain interactions throughout the entire brain, whereas AD is distinguished by additional diminished posterior DMN-precuneus coherence.