Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic and Automated Multiprocessor System Design, Programming, and Implementation

For modern embedded systems in the realm of high-throughput multimedia, imaging, and signal processing, the complexity of embedded applications has reached a point where the performance requirements of these applications can no longer be supported by embedded system architectures based on a single processor. Thus, the emerging embedded system-on-chip platforms are increasingly becoming multiprocessor architectures. As a consequence, two major problems emerge, namely how to design and how to program such multiprocessor platforms in a systematic and automated way in order to reduce the design time and to satisfy the performance needs of applications executed on such platforms. As an efficient solution to these two problems, in this paper, we present the methodology and techniques implemented in a tool called Embedded System-level Platform synthesis and Application Mapping (ESPAM) for automated multiprocessor system design, programming, and implementation. ESPAM moves the design specification and programming from the Register Transfer Level and low-level C to a higher system level of abstraction. We explain how, starting from system-level platform, application, and mapping specifications, a multiprocessor platform is synthesized, programmed, and implemented in a systematic and automated way. The class of multiprocessor platforms we consider is introduced as well. To validate and evaluate our methodology, we used ESPAM to automatically generate and program several multiprocessor systems that execute three image processing applications, namely Sobel edge detection, Discrete Wavelet Transform, and Motion JPEG encoder. The performance of the systems that execute these applications is also presented in this paper.
A Novel Progressive Transmission in Mobile Visual Search

How to reduce the transmission latency is a main concern in the context of Mobile Visual Search (MVS). Transmitting extremely compacted visual descriptor in a progressive manner is the start-of-art solution. In this paper, we present a novel MVS system following the client-server architecture. To reduce the transmission latency, the inquiry image is represented by a set of hash bits, which are then progressively transmitted. In the server side, all images are indexed by their hash bits, similar as the classic Bag-of-Word (BoW) model. Owe to the merit of the proposed system, the IDF weight of the hash bits are encoded into a sparse vector which retained in the mobile client, and provides a transmission order of the inquiry hash bits. The hash bit with lower IDF weight will be more discriminative, which should have higher priority during the transmission. As far as we know, this work is the first one attempting to transmit the hash bits in a proper progressive manner in MVS. Extensive experiments have been done on the public Stanford MVS database, demonstrating that the proposed progressive transmission strategy achieves higher recognition rate compared to other strategies, when delivering the same amount of data.
Multi-path Convolutional Neural Networks for Complex Image Classification

Convolutional Neural Networks demonstrate high performance on ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the published results only show the overall performance for all image classes. There is no further analysis why certain images get worse results and how they could be improved. In this paper, we provide deep performance analysis based on different types of images and point out the weaknesses of convolutional neural networks through experiment. We design a novel multiple paths convolutional neural network, which feeds different versions of images into separated paths to learn more comprehensive features. This model has better presentation for image than the traditional single path model. We acquire better classification results on complex validation set on both top 1 and top 5 scores than the best ILSVRC 2013 classification model.
Evaluation of Three Tablet Computers at Two Levels of Ambient Illumination

Background: Tablet computers are increasingly widespread in this digital era. During tablet computer use, two important factors are display quality and ambient illumination. The purpose of this experiment was to understand the effects of the display quality of tablet computers (a Sony, a Samsung, and an iPad) and ambient illumination levels (200 lx, 500 lx) on visual fatigue, comfort perception, and task performance. Methods: Thirty-five university students (17 males and 18 females) were recruited for this experiment. They performed six 40-min trials of playing computer games in all experimental conditions. Visual fatigue, comfort perception, and task performance were collected at the end of each trial. Results and Discussion: There were no interactions between display model and ambient illumination. Display model had a significant effect on visual fatigue and visual performance, and the best display quality caused less visual fatigue during long periods of viewing. We found no significant effect between 200 lx and 500 lx of ambient illumination on visual fatigue or visual performance; therefore, further research should be conducted to determine what levels of ambient illumination, both high and low, can affect visual fatigue and task performance.
DID is trauma based: further evidence supporting the trauma model of DID

exposure (TEC and TEC subscale measures), and problematic with (lower care and higher paternal overprotection subscales of the PBI), compared to all three groups. The DID-G group also had significantly lower scores for attachment to mothers (lower maternal care subscale of the PBI) compared to the DID-S and HC groups, but not the PTSD group. Our results indicate that the DID patients had higher dissociation and trauma exposure as well greater attachment insecurity with mothers and fathers after controlling for potential malingering. All of these analyses support the Trauma Model. A MANCOVA controlling for SIMS scores further demonstrated significant group differences on the ISES general subscale, and the neurologic, low intelligence, and psychosis subscales of the SIMS. There were no group differences for fantasy proneness, the ISES-lucid dreaming subscale, or the affective and amnesia subscales of the SIMS. Post-hoc pairwise comparisons revealed that the DID-G group reported more sleeping problems (ISES-general subscale) and had higher SIMS-low intelligence subscale scores than HC participants. DID-G participants scored higher on the SIMS-neurologic and psychosis subscales compared to PTSD participants. Post-hoc pairwise group differences were not found to be significant between the DID-G and the DID-S group on sleep problems, or the SIMS neurologic, psychosis or low intelligence subscales. The DID-G group did not differ significantly from the PTSD group on the low intelligence SIMS subscale nor sleep problems. Lastly, the DID-G group did not differ significantly from the HC group on the neurologic nor psychosis subscales of the SIMS. Consistent with our findings, research demonstrates that DID have high levels of psychotic due to hearing voices of states and high level of actually sensorimotor 32). The lack of group that are not elevated
Simulating and modeling lichen growth

This paper presents a system for modeling lichens and simulating their propagation and growth in a virtual scene. Lichens colonize almost every substrate in nature and play an important role in the visual appearance of a natural object. The propagation of lichens over the substrate is performed by an Open Diffusion Limited Aggregation model constrained by the characteristics of the environment. The designer can control the development of lichens with simple parameters. Rendering the complex geometry and texture of lichens is achieved by instantiating three dimensional lichen models stored in an atlas of shapes created after real world images. The lichens obtained by our approach considerably increase the realism of complex natural scenes.
Purification and Some Properties of an Extracellular Amylase from a Moderate Halophile, Micrococcus halobius

A moderate halophile, Micrococcus halobius ATCC 21727, produced an extracellular dextrinogenic amylase when cultivated in media containing 1 to 3 M NaCl. The amylase was purified from the culture filtrate to an electrophoretically homogenous state by glycogen-complex formation, diethylaminoethyl-cellulose chromatography, and Bio-Gel P-200 gel filtration. The enzyme had maximal activity at pH 6 to 7 in 0.25 M NaCl or 0.75 M KCl at 50 to 55°C. The activity was lost by dialysis against distilled water. Molecular weight was estimated to be 89,000 by sodium dodecyl sulfate-gel electrophoresis. The action pattern on amylose, soluble starch, and glycogen showed that the products were maltose, maltotriose, and maltotetraose, with lesser amount of glucose.
Hyperglycemic clamp and oral glucose tolerance test for 3-year prediction of clinical onset in persistently autoantibody-positive offspring and siblings of type 1 diabetic patients.

CONTEXT AND OBJECTIVE In preparation of future prevention trials, we aimed to identify predictors of 3-year diabetes onset among oral glucose tolerance test (OGTT)- and hyperglycemic clamp-derived metabolic markers in persistently islet autoantibody positive (autoAb(+)) offspring and siblings of patients with type 1 diabetes (T1D). DESIGN The design is a registry-based study. SETTING Functional tests were performed in a hospital setting. PARTICIPANTS Persistently autoAb(+) first-degree relatives of patients with T1D (n = 81; age 5-39 years). MAIN OUTCOME MEASURES We assessed 3-year predictive ability of OGTT- and clamp-derived markers using receiver operating characteristics (ROC) and Cox regression analysis. Area under the curve of clamp-derived first-phase C-peptide release (AUC(5-10 min); min 5-10) was determined in all relatives and second-phase release (AUC(120-150 min); min 120-150) in those aged 12-39 years (n = 62). RESULTS Overall, the predictive ability of AUC(5-10 min) was better than that of peak C-peptide, the best predictor among OGTT-derived parameters (ROC-AUC [95%CI]: 0.89 [0.80-0.98] vs 0.81 [0.70-0.93]). Fasting blood glucose (FBG) and AUC(5-10 min) provided the best combination of markers for prediction of diabetes within 3 years; (ROC-AUC [95%CI]: 0.92 [0.84-1.00]). In multivariate Cox regression analysis, AUC(5-10 min)) (P = .001) was the strongest independent predictor and interacted significantly with all tested OGTT-derived parameters. AUC(5-10 min) below percentile 10 of controls was associated with 50-70% progression to T1D regardless of age. Similar results were obtained for AUC(120-150 min). CONCLUSIONS Clamp-derived first-phase C-peptide release can be used as an efficient and simple screening strategy in persistently autoAb(+) offspring and siblings of T1D patients to predict impending diabetes.
A Least Absolute Bound Approach to ICA - Application to the MLSP 2006 Competition

This paper describes a least absolute bound approach as a way to solve the ICA problems proposed in the 2006 MSLP competition. The least absolute bound is an ICA contrast closely related to the support width measure, which has been already studied for the blind extraction of bounded sources. By comparison, the least absolute bound applies to a broader class of sources, including those that are bounded on a single side only. This precisely corresponds to the sources involved in the competition. Practically, the minimization of the least absolute bound relies on a specific deflation algorithm with a loose orthogonality constraint. This allows solving large-scale problems without accumulating errors.
An Insight into the Sialotranscriptome of the Cat Flea, Ctenocephalides felis

Background Saliva of hematophagous arthropods contains a diverse mixture of compounds that counteracts host hemostasis. Immunomodulatory and antiinflammatory components are also found in these organisms' saliva. Blood feeding evolved at least ten times within arthropods, providing a scenario of convergent evolution for the solution of the salivary potion. Perhaps because of immune pressure from hosts, the salivary proteins of related organisms have considerable divergence, and new protein families are often found within different genera of the same family or even among subgenera. Fleas radiated with their vertebrate hosts, including within the mammal expansion initiated 65 million years ago. Currently, only one flea species–the rat flea Xenopsylla cheopis–has been investigated by means of salivary transcriptome analysis to reveal salivary constituents, or sialome. We present the analysis of the sialome of cat flea Ctenocephaides felis. Methodology and Critical Findings A salivary gland cDNA library from adult fleas was randomly sequenced, assembled, and annotated. Sialomes of cat and rat fleas have in common the enzyme families of phosphatases (inactive), CD-39-type apyrase, adenosine deaminases, and esterases. Antigen-5 members are also common to both sialomes, as are defensins. FS-I/Cys7 and the 8-Cys families of peptides are also shared by both fleas and are unique to these organisms. The Gly-His-rich peptide similar to holotricin was found only in the cat flea, as were the abundantly expressed Cys-less peptide and a novel short peptide family. Conclusions/Significance Fleas, in contrast to bloodsucking Nematocera (mosquitoes, sand flies, and black flies), appear to concentrate a good portion of their sialome in small polypeptides, none of which have a known function but could act as inhibitors of hemostasis or inflammation. They are also unique in expansion of a phosphatase family that appears to be deficient of enzyme activity and has an unknown function.
Transferring Coreference Resolvers with Posterior Regularization

We propose a cross-lingual framework for learning coreference resolvers for resource-poor target languages, given a resolver in a source language. Our method uses word-aligned bitext to project information from the source to the target. To handle task-specific costs, we propose a softmax-margin variant of posterior regularization, and we use it to achieve robustness to projection errors. We show empirically that this strategy outperforms competitive cross-lingual methods, such as delexicalized transfer with bilingual word embeddings, bitext direct projection, and vanilla posterior regularization.
Comparative analysis of procedural approaches for facility layout design using AHP approach

The purpose of this paper is to apply multiple attribute decision-making (MADM) approach (analytic hierarchy process - AHP) for ranking the procedural approaches available for solving the facility layout design problems (FLPs). Analytic hierarchy process is applied to rank the approaches, whereas the weight to a factor is assigned using modified digital logic (MDL) method. On the basis of assigned weight the matrix is generated, and the factors considered for analysis are as: initial data required (IDR), use of charts (UC), use of graphs and diagrams (UG), future expansion considered (FE), constraints considered (CC), procedure implementation (PI), and material handling equipment selection considered (MH). The approaches which have been compared are as: Nadler's procedure (NP), Immer's procedure (IP), Muther's procedure (MP), Apple's procedure (AP), and Reed's procedure (RP). The results of the present study demonstrate that MP gets highest rank among the compared procedures. The output ranks along with their overall priority from the present study are as: MP (0.355) > AP (0.232) > RP (0.228) > NP (0.098) > IP (0.088). The factors which are used in this study are weighted in the order as: UC (0.22) > UG (0.195) > MH (0.171) > PI (0.146) > CC (0.098) = IDR (0.098) > FE (0.073).
Security and Results of a Large-Scale High-Interaction Honeypot

This paper presents the design and discusses the results of a secured high-interaction honeypot. The challenge is to have a honeypot that welcomes attackers, allows userland malicious activities but prevents system corruption. The honeypot must authorize real malicious activities. It must ease the analysis of those activities. A clustered honeypot is proposed for two kinds of hosts. The first class prevents a system corruption and never has to be reinstalled. The second class assumes a system corruption but an easy reinstallation is available. Various off-the-shelf security tools are deployed to detect a corruption and to ease analysis. Moreover, host and network information enable a full analysis for complex scenario of attacks. The solution is totally based on open source software and has been validated over two years. A complete analysis is provided using the collected events and alarms. First, different types of malicious activities are easily reconstructed. Second, correlation of alarms enables us to compare the efficiency of various off-the-shelf security tools. Third, a correlation eases a complete analysis for the host and network activities. Finally, complete examples of attacks are explained. Ongoing works focus on recognition of complex malicious activities using a correlation grid and on distributed analysis.
Fast stitching algorithm for moving object detection and mosaic construction

This paper proposes a novel edge-based stitching method to detect moving objects and construct mosaics from images. The method is a coarse-to-fine scheme which first estimates a good initialization of camera parameters with two complementary methods and then refines the solution through an optimization process. The two complementary methods are the edge alignment and correspondence-based approaches, respectively. Since these two methods are complementary to each other, the desired initial estimate can be obtained more robustly. After that, a Monte-Carlo style method is then proposed for integrating these two methods together. Then, an optimization process is applied to refine the above initial parameters. Since the found initialization is very close to the exact solution and only errors on feature positions are considered for minimization, the optimization process can be very quickly achieved. Experimental results are provided to verify the superiority of the proposed method.
ELI: bare-metal performance for I/O virtualization

Direct device assignment enhances the performance of guest virtual machines by allowing them to communicate with I/O devices without host involvement. But even with device assignment, guests are still unable to approach bare-metal performance, because the host intercepts all interrupts, including those interrupts generated by assigned devices to signal to guests the completion of their I/O requests. The host involvement induces multiple unwarranted guest/host context switches, which significantly hamper the performance of I/O intensive workloads. To solve this problem, we present ELI (ExitLess Interrupts), a software-only approach for handling interrupts within guest virtual machines directly and securely. By removing the host from the interrupt handling path, ELI manages to improve the throughput and latency of unmodified, untrusted guests by 1.3x-1.6x, allowing them to reach 97%-100% of bare-metal performance even for the most demanding I/O-intensive workloads.
Path Planning for Robot Navigation u sing View Sequences

Navigation based on visual memories is very common among humans. However, planning long trips requires also a more sophisticated represen- tation of the environment, such as a topological map. This paper describes a system that learns paths by storing sequences of images and image information in a Sparse Distributed Memory. Connections between paths are detected by exploring similarities in the im- ages, and a topological representation of the connec- tions is created. The robot is then able to plan paths and skip from one path to another at the connection points. The system was tested under reconstitutions of country and urban environments, and it was able to successfully plan paths and navigate.
Recognition of facial expressions : Selective impairment of specific emotions in Huntington's disease

Recognition of facial expressions of basic emotions was investigated in HL and UJ, two people with Huntington's disease who showed little evidence of general cognitive deterioration. No impairments were found in tests of the perception of age, sex, familiar face identity, unfamiliar face identity, and gaze direction, indicating adequate processing of the face as a physical stimulus. Computer-interpolated ("morphed") images of facial expressions of basic emotions were used to demonstrate deficits in the recognition of disgust and fear for UJ. HL also showed a deficit in the recognition of disgust, and was not very adept (but not significantly impaired) at recognising fear. Other basic emotions (happiness, surprise, sadness, anger) were recognised atnormal levels of performance by HL and UJ. These results show that impairments of emotion recognition can be circumscribed; affecting some emotions more than others, and occurring in people who do not show pronounced perceptual or intellectual deterioration. Que.
Network Intrusion Detection System Using Neural Network Classification of Attack Behavior

Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based Intrusion Detection System (HIDS), which protects a certain host or system and Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems. This paper addresses Probes attacks or reconnaissance attacks, which try to collect any possible relevant information in the network. Network probe attacks have two types: Host Sweep and Port Scan attacks. Host Sweep attacks determine the hosts that exist in the network, while port scan attacks determine the available services that exist in the network. This paper uses an intelligent system to maximize the recognition rate of network attacks by embedding the temporal behavior of the attacks into a TDNN neural network structure. The proposed system consists of five modules: packet capture engine, preprocessor, pattern recognition, classification, and monitoring and alert module. Our system uses Principle Component neural network for recognizing attacks and a classification module to classify the attacks into host sweep or port scan. We have tested the system in a real environment where it was able to detect all attacks. In addition, the system was tested and compared with SNORT using DARPA datasets. Our system outperforms SNORT in terms of recognition rate and throughput. In fact, our system can recognize all attacks in a constant time.
Non-linear optimization for multi-path source routing in obs networks

Optical burst switching (OBS) architectures without buffering capabilities are sensitive to burst losses. A proper routing strategy may help to reduce such congestion. In this letter we present a novel approach for OBS multi-path source routing problem. Our solution is based on non-linear optimization with a straightforward calculation of partial derivatives. Simulation results demonstrate that optimized routing effectively reduces the overall burst loss probability with respect to shortest path and deflection routing.