Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence analysis of cDNA's derived from the RNA of Sindbis virions and of defective interfering particles

Sindbis virus generates defective interfering (DI) particles during serial high-multiplicity passage in cultured cells. These DI particles inhibit the replication of infectious virus and can be an important factor in the establishment and maintenance of persistent infection in BHK cells. In an effort to understand how these DI particles are generated and how they interfere with the replication of standard virus, we performed a partial sequence analysis of the RNA obtained from two independently isolated populations of DI particles and from two Sindbis virus variants and compared these with the RNA of the parental wild-type virus. The 3'-terminal regions of the RNAs were sequenced by the dideoxy chain terminating method. Internal regions of the RNA were examined by restriction endonuclease digestion of cDNA's made to the various RNAs and by direct chemical sequencing of 5' end-labeled restriction fragments from cDNA made to the DI RNAs. One of the variant viruses examined was originally derived from cells persistently infected with Sindbis virus for 16 months and is resistant to interference by the DI strains used. In the 3'-terminal region of the RNA from this variant, only two base changes were found; one of these occurs in the 20-nucleotide 3'-terminal sequence which is highly conserved among alphaviruses. The DI RNA sequences were found to have been produced not by a single deletional event, but by multiple deletion steps combined with sequence rearrangements; all sequences examined are derived from the plus strand of Sindbis virion RNA. Both DI RNAs had at least 50 nucleotides of wild-type sequence conserved at the 3' terminus; in addition, they both contained conserved and perhaps amplified sequences derived from the non-26S region of the genome which may be of importance in their replication and interference ability.
Analysis of Theoretical Relationships between Learning Styles of Students and Their Preferences for Learning Activities.

Human beings differ from one another in numerous ways. The demands of a particular situation drawn upon aptitudes that create individual differences in a person’s response to a problem. A students performance in education is a product of whatever mixture of predispositions he/she brings to that performance that interact with the educational tasks and situations presented. Perkins (1985) alluded to some of these predispositions when he stated:
Effect of different constraints on coordination and performance in overarm throwing.

This thesis investigated the effect of the following constraints: body anthropometrics, gender, maximal isometric strength, type of training program, instruction and ball weight. These constraints were investigated in relation to the coordination and performance (i.e. velocity and accuracy) in overarm throwing of experienced handball players. Instruction emphasising accuracy and/or velocity of throwing performance influenced the maximal ball velocity but showed no effect on accuracy. The difference in ball velocity was a result of the difference in maximal linear velocity of the upper body segments together with their timing. The research also showed that body size had a strong positive effect on the throwing performance and isometric strength. Throwing velocity appeared to be affected by gender when size was expressed by mass or height. However, this dependence was completely explained for by size differences when expressed as fat free body mass (FFM). For strength, no gender effect was found at all. This finding strengthens the notion that gender difference is based on difference in muscle bulk and that FFM, as an approximation for skeletal muscle mass, is the best measure to express body size when related to physical performance. Projectile mass affects the release velocity. A linear negative relationship between ball mass and maximal ball velocity, as well as between force and maximal ball velocity was observed. The main contributors to the total ball velocity at release were the internal rotation of the shoulder (63 ) and elbow extension (32 ). Furthermore, it was found that technique was not affected when the ball mass was increased nor when instructions were changed, as was indicated by the invariance of the relative timing of the upper body segments. There are also found indications that the generally accepted proximal-distal sequence did not exist in overarm throwing. No clear answer can be given to the question which type of strength training increases throwing performance (velocity). However, training studies that involved throwing with underweight balls seemed to have a clear positive effect on the throwing velocity. Training with overweight balls, medicine balls or general strength training with load lower than 12 RM gave contradictive results.
Automatically Constructing a Dictionary for Information Extraction Tasks

Knowledge-based natural language processing systems have achieved good success with certain tasks but they are often criticized because they depend on a domain-specific dictionary that requires a great deal of manual knowledge engineering. This knowledge engineering bottleneck makes knowledge-based NLP systems impractical for real-world applications because they cannot be easily scaled up or ported to new domains. In response to this problem, we developed a system called AutoSlog that automatically builds a domain-specific dictionary of concepts for extracting information from text. Using AutoSlog, we constructed a dictionary for the domain of terrorist event descriptions in only 5 person-hours. We then compared the AutoSlog dictionary with a hand-crafted dictionary that was built by two highly skilled graduate students and required approximately 1500 person-hours of effort. We evaluated the two dictionaries using two blind test sets of 100 texts each. Overall, the AutoSlog dictionary achieved 98% of the performance of the hand-crafted dictionary. On the first test set, the AutoSlog dictionary obtained 96.3% of the performance of the hand-crafted dictionary. On the second test set, the overall scores were virtually indistinguishable with the AutoSlog dictionary achieving 99.7% of the performance of the handcrafted dictionary.
Survivable communications and autonomous delivery service a generic swarming framework enabling communications in contested environments

This paper presents an agile and generic decentralized swarming architecture called the Survivable Communications and Autonomous Delivery Service (SCADS). SCADS enables swarm members to modify their individual (local) behavior to best contribute to meeting global mission objectives. In this paper we examine how the architecture can provide an agile swarming approach to data ferrying to facilitate information exchange in a highly contested radio frequency (RF) environment. The SCADS architecture is described and the implementation of a data ferrying algorithm within the framework on multiple unmanned autonomous systems is presented. We demonstrate how this approach can disseminate data using a delay tolerant networking paradigm to efficiently deliver data in a sparse disconnected network topology in the presence RF jamming.
Analysis of performances of InSb HEMTs using quantum-corrected Monte Carlo simulation

The strained band structures of InSb are calculated, and then the DC and RF performances of the InSb HEMTs are analyzed by using the quantum-corrected Monte Carlo (MC) simulation. These are also compared with the InAs HEMTs. Although the compressive strain applied to the channel increases the electron effective mass, m*, the InSb HEMTs still show the higher current drivability and the higher fT than the InAs HEMTs from the lower Vds. However, the severe impact ionization occurs from the lower Vds owing to the smaller impact ionization threshold energy, Eth, although the compressive strain increases it. This restricts the InSb HEMTs within the low Vds applications.
Efficient Context-Sensitive Intrusion Detection

Model-based intrusion detection compares a process’s execution against a program model to detect intrusion attempts. Models constructed from static program analysis have historically traded precision for efﬁciency. We address this problem with our Dyck model, the ﬁrst efﬁ-cient statically-constructed context-sensitive model. This model speciﬁes both the correct sequences of system calls that a program can generate and the stack changes occurring at function call sites. Experiments demonstrate that the Dyck model is an order of magnitude more precise than a context-insensitive ﬁnite state machine model. With null call squelching, a dynamic technique to bound cost, the Dyck model operates in time similar to the context-insensitive model. We also present two static analysis techniques designed to counter mimicry and evasion attacks. Our branch analysis identiﬁes between 32% and 64% of our test programs’ system call sites as affecting control ﬂow via their return values. Interprocedural argument capture of general values recovers 32% to 69% more arguments than previously reported techniques.
Animal models of tuberculosis

There is a wide range of animal models of tuberculosis; which is fortunate, given that there is a wide range of complex biological phenomena to model. Nonetheless, the inadequacy of animal models is a chronic grievance amongst researchers, often expressed in the conclusion that the mouse is not a good model of human tuberculosis. There clearly are major differences between experimental infection of mice and natural infection of humans, but this overarching dogma is inconducive to fruitful research.
Multi-modal video concept detection for scalable logging of pre-production broadcast content

We present a scalable semantic video concept detection framework, applied to automated metadata annotation (video logging) in a broadcast production environment. Video logging demands both accurate and fast concept detection. Whilst research often focuses on the former, the latter is essential in practical scenarios where days of footage may be shot per broadcast episode and production is dependent on immediate availability of metadata. We present a hierarchical classification framework that delivers benefits to both through two contributions. First, a dynamic weighting scheme for combining video features from multiple modalities enabling higher accuracy detection rates over diverse production footage. Second, a hierarchical classification strategy that exploits ontological relationships between concepts to scale sub-linearly with the number of classes, yielding a real-time solution. We demonstrate an end-to-end production system incorporating chronological and semantic browsing with our detection framework. Demo video included.
Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework

We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time-independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning to hit a ball with a robotic arm, and of teaching a humanoid robot to manipulate a spoon to feed another humanoid. The experiments demonstrate that the proposed model can efficiently handle several aspects of learning by imitation. We first show that it can be utilized in an unsupervised learning manner, where the robot is autonomously organizing and encoding variants of motion from the multiple demonstrations. We then show that the approach allows to robustly generalize the observed skill by taking into account multiple constraints in task space during reproduction.
Spatial game analytics and visualization

The recently emerged field of game analytics and the development and adaptation of business intelligence techniques to support game design and development has given data-driven techniques a direct role in game development. Given that all digital games contain some sort of spatial operation, techniques for spatial analysis had their share in these developments. However, the methods for analyzing and visualizing spatial and spatio-temporal patterns in player behavior being used by the game industry are not as diverse as the range of techniques utilized in game research, leaving room for a continuing development. This paper presents a review of current work on spatial and spatio-temporal game analytics across industry and research, describing and defining the key terminology, outlining current techniques and their application. We summarize the current problems and challenges in the field, and present four key areas of spatial and spatio-temporal analytics: Spatial Outlier Detection, Spatial Clustering, Spatial Predictive Models, Spatial Pattern and Rule Mining. All key areas are well-established outside the context of games and hold the potential to reshape the research roadmap in game analytics.
Neurokinin-1 receptor antagonists as novel antidepressants: trials and tribulations

Summary Based upon animal experiments and early clinical trials, neurokinin-1 receptor antagonists showed promise as novel antidepressants. Subsequently, however, more extensive clinical trials did not reveal evidence of efficacy in depression. The development of novel antidepressants will require a better understanding of the neural basis of antidepressant action in humans.
Towards online review spam detection

User reviews play a crucial role in Web, since many decisions are made based on them. However, review spam would misled the users, which is extremely obnoxious. In this poster, we explore the problem of online review spam detection. Firstly, we devise six features to find the spam based on the review content and reviewer behaviors. Secondly, we apply supervised methods and an unsupervised one for spotting the review spam as early as possible. Finally, we carry out intensive experiments on a real-world review set to verify the proposed methods.
Ether: malware analysis via hardware virtualization extensions

Malware has become the centerpiece of most security threats on the Internet. Malware analysis is an essential technology that extracts the runtime behavior of malware, and supplies signatures to detection systems and provides evidence for recovery and cleanup. The focal point in the malware analysis battle is how to detect versus how to hide a malware analyzer from malware during runtime. State-of-the-art analyzers reside in or emulate part of the guest operating system and its underlying hardware, making them easy to detect and evade. In this paper, we propose a transparent and external approach to malware analysis, which is motivated by the intuition that for a malware analyzer to be transparent, it must not induce any side-effects that are unconditionally detectable by malware. Our analyzer, Ether, is based on a novel application of hardware virtualization extensions such as Intel VT, and resides completely outside of the target OS environment. Thus, there are no in-guest software components vulnerable to detection, and there are no shortcomings that arise from incomplete or inaccurate system emulation. Our experiments are based on our study of obfuscation techniques used to create 25,000 recent malware samples. The results show that Ether remains transparent and defeats the obfuscation tools that evade existing approaches.
Survey and taxonomy of IP address lookup algorithms

Due to the rapid growth of traffic in the Internet, backbone links of several gigabits per second are commonly deployed. To handle gigabit-per-second traffic rates, the backbone routers must be able to forward millions of packets per second on each of their ports. Fast IP address lookup in the routers, which uses the packet's destination address to determine for each packet the next hop, is therefore crucial to achieve the packet forwarding rates required. IP address lookup is difficult because it requires a longest matching prefix search. In the last couple of years, various algorithms for high-performance IP address lookup have been proposed. We present a survey of state-of-the-art IP address lookup algorithms and compare their performance in terms of lookup speed, scalability, and update overhead.
Two-Factor Authentication and Its Adaptation to Online Education Systems

This study examines the evolution of the two-factor authentication method and its adaptability to the online education system. Two-factor authentication is a security measure used especially in areas where information such as banking is valuable. Parallel to technological developments, it has developed as much as daily. It aims to take security one step forward because it is composed of two phases. Today, banking, IOT devices, public transport tickets and many other areas are used. Two-factor authentication methods against security attacks in the field of information are also being updated. In recent years, new technologies such as biometric (iris pattern, retinal pattern, etc.) or behavioral biometry (location tracking, walking information, touch speed etc.) were studied. Instead of physically studying somewhere like going to a course in modern society, online trainings become more advantageous. Most of these online trainings are given certificates such as participation certificate, success certificate, etc. The main problem here is whether the person who is being certified is true. In the study conducted in line with these details, there is a proposal for the application of the Mimic Control Method with Sound Intensity (MCMSI) method for on-line training by examining the two-factor authentication techniques up to the day.
Disk Density Tuning of a Maximal Random Packing

We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson‐disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more‐aggressive local operations. We may achieve a user‐defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict‐free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially‐varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non‐obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.
DyRT: dynamic response textures for real time deformation simulation with graphics hardware

In this paper we describe how to simulate geometrically complex, interactive, physically-based, volumetric, dynamic deformation models with negligible main CPU costs. This is achieved using a Dynamic Response Texture, or DyRT, that can be mapped onto any conventional animation as an optional rendering stage using commodity graphics hardware. The DyRT simulation process employs precomputed modal vibration models excited by rigid body motions. We present several examples, with an emphasis on bone-based character animation for interactive applications.
Author ' s personal copy Activated cortical states : Experiments , analyses and models

In awake animals, the cerebral cortex displays an ‘‘activated’’ state, with distinct characteristics compared to other states like slow-wave sleep or anesthesia. These characteristics include a sustained depolarized membrane potential (Vm) and irregular firing activity. In the present paper, we evaluate our understanding of cortical activated states from a computational neuroscience point of view. We start by reviewing the electrophysiological characteristics of activated cortical states based on recordings and analysis performed in awake cat association cortex. These analyses show that cortical activity is characterized by an apparent Poisson-distributed stochastic dynamics, both at the single-cell and population levels, and that single cells display a high-conductance state dominated by inhibition. We next overview computational models of the ‘‘awake’’ cortex, and perform the same analyses as in the experiments. Many properties identified experimentally are indeed reproduced by models, such as depolarized Vm, irregular firing with apparent Poisson statistics, and the determinant role of inhibitory fluctuations on spiking. However, other features are not well reproduced, such as firing statistics and the conductance state of the membrane, suggesting that the network state displayed by models is not entirely correct. We also show how networks can approach a correct conductance state, suggesting ways by which future models will generate activity fully consistent with experimental data. 2007 Elsevier Ltd. All rights reserved.
A new method of regularization parameter estimation for source localization

The problem of estimating the regularization parameter for source localization in sparse-regularization framework is considered in this paper. We employ the distribution about every entry of the square of the Frobenius norm of noise to obtain a larger and more appropriate regularization parameter. The paper analyzes the reason that we can not simply set it equal to the square of the Frobenius norm of noise and presents the estimation in two practical cases: one works without taking singular value decomposition (SVD) of sensor outputs; the other works after that pretreatment for large data quantity. The simulation results demonstrate that the proposed method has many advantages, including enhancing resolution, effectively suppressing spurious peaks, improving robustness to noise, as well as increasing the number of resolvable sources.
On Optimal Scheduling in Duty-Cycled Industrial IoT Applications Using IEEE802.15.4e TSCH

As exposed in a recent report by General Electric, an industrial Internet of Things (IoT) is emerging as a commercially viable embodiment of the IoT where physical sensors gather data readings from the field and deliver the traffic to the Internet. The collected real-time big data, in turn, allow the optimizing of entire industry verticals with enormous return of investments. Although opportunities are ample, it comes along with serious engineering design challenges as industrial applications have stringent requirements on delay, lifetime and standards-compliance. To this end, we advocate the use of an IEEE/IETF standardized IoT architecture along with a recently introduced data-centric scheduling algorithm known as traffic aware scheduling algorithm (TASA). Applying graph theoretical tools to the multi-channel, time-synchronized, and duty-cycled nature of TASA, we rigorously derive optimality and bounds on the minimum number of needed active slots (impacting end-to-end delays) and the network duty-cycle (impacting lifetime). We demonstrate the enormous superiority of TASA over traditional IEEE802.15. 4/ZigBee approaches in terms of energy efficiency. The outcome of this paper is currently to lay foundations of the recently formed IETF standardization group 6TSCH with the aim to significantly improve IoT data flows over IEEE802.15. 4e TSCH and IETF 6LoWPAN/ROLL enabled technologies.
Geometric and Signal Strength Dilution of Precision (DoP)Wi-Fi

The democratization of wireless networks combined to the emergence of mobile devices increasingly autonomous and efficient lead to new services. Positioning services become overcrowded. Accuracy is the main quality criteria in positioning. But to better appreciate this one a coefficient is needed. In this paper we present Geometric and Signal Strength Dilution of Precision (DOP) for positioning systems based on Wi-Fi and Signal Strength measurements.