Miracle-dz/UniFAR-FTUs huggingface dataset:
Code Rocket : Seeking improvements in detailed design support for non-model driven approaches to development

Code Rocket is a support tool for software developers which allows them to perform the detailed design of a software system and produce program code simultaneously. It integrates with existing development environments to allow developers to visualize program code during code construction and code maintenance. Code Rocket supports other project stakeholders, including nonprogrammers, by providing improved visibility of the detailed stages of code development in a way that is quick and easy to assimilate. Code Rocket seeks to bridge the detailed design gap between UML and code in mainstream software development, for projects which do not adopt formal or model-driven approaches. This paper introduces Code Rocket, describes the background and rationale for its development and presents the results of a survey of industry users which suggest that it may offer benefits for improving visibility of business logic within code, assisting comprehension of code, and helping developers to become productive more quickly.
Beautiful dance moves: mapping movement, technology & computation

This Creativity & Cognition 2013 workshop explores emerging methods for mapping movement, technology and computation. We invite participants that are interested in bodily experience within computational knowledge representation. The title Beautiful Dance Moves, references the challenge of representing embodied movement knowledge within computational models. While human movement itself focuses on bodily experience, developing computational models for movement requires abstraction and representation of lived embodied cognition. Mappings between movement and its rich personal and cultural meanings provide an underexplored research domain that can provide insight within computational modeling. Many fields, including Interaction Design, have been inspired by recent developments within Neuroscience validating the primacy of movement in cognitive development and human intelligence. This has spawned a growing interest in experiential principles of movement awareness and mindfulness, while simultaneously fueling the need for developing computational models that can describe movement intelligence with greater rigour. This workshop seeks to explore an equal and richly nuanced epistemological partnership between movement experience and movement cognitive and computational representation.
Independent One-Time Passwords

Existing one-time password (OTP) schemes suffer several drawbacks. Token-based systems are expensive, while software-based schemes rely on one-time passwords that are dependent on each other. There are disadvantages to authentication schemes that rely on dependent OTP's. It is difficult to replicate the authentication server without lowering security. Also, current authentication schemes based on dependent OTP's only authenticate the initial connection; the remainder of the session is assumed to be authenticated. Experience shows that connections can be hijacked. A new scheme for generating one-time passwords that are independent is presented. The independence property enables easy replication of the authentication server, and authentication that is persistent for the lifetime of a connection. This mechanism is also ideally suited for smart card applications. Our implementation and several applications are discussed.
Minkowski sum based lattice construction for solving simultaneous modular equations and applications to RSA

We investigate a lattice construction method for the Coppersmith technique for finding small solutions of a modular equation. We consider its variant for simultaneous equations and propose a method to construct a lattice by combining lattices for solving single equations. As applications, we consider (i) a new RSA cryptanalysis for multiple short secret exponents, (ii) its partial key exposure situation, and (iii) investigating the hardness of finding a certain amount of LSBs of the RSA secret exponent. More precisely, our algorithm can factor an RSA modulus from l ≥ 2 pairs of RSA public exponents with the common modulus corresponding to secret exponents smaller than N (9l−5)/(12l+4), which improves on the previously best known result N (3l−1)/(4l+4) by Sarkar and Maitra [41, 42]. For partial key exposure situation, we also can factor the modulus if β − δ/2 + 1/4 < (3l − 1)(3l + 1), where β and δ are bit-lengths /n of the secret exponent and its exposed LSBs, respectively. Particularly, letting β = 1, which means that the secret exponent is full-sized, the necessary amount of exposed bits is [5/2− 2(3l− 1)/(3l+ 1)]n, which is less than n for l ≥ 3. Suppose we have an algorithm that recovers the above amount of d from e and N satisfying e ≈ N . We showed that N can be factored in polynomial time in logN under a heuristic assumption that the Coppersmith technique works. When l becomes large, the necessary amount becomes 0.5n bits. Hence, we conclude that recovering the lower half of LSBs of d is polynomial time equivalent to the factoring under the heuristic assumption. From the last result, we propose a half-amount conjecture that roughly, factoring RSA modulus is polynomial-time equivalent to any continued bits of secret information such as p, q, d, p + q and p − q (or dp and dq for RSA-CRT). It is supported from several results, e.g., Coppersmith [12] shows that recovering the upper half of p is equivalent to factoring.
Methods to Improve the Performances of the Real-Time Operating Systems for Small Microcontrollers

The use of real-time operating systems for small micro controllers creates two major problems. The former refers to the way interruptions must be handled so that deadlines can be complied with. The latter regards the fact that there may be tasks that will not synchronize with events generated by multiple objects such as semaphores, mutexes, messages or interrupts. The present article discusses several solutions to improve the performance of Real Time Operating Systems for small micro controllers and eventually overcome the above-mentioned inconveniences.
Chemical characteristics of North American surface layer outflow: Insights from Chebogue Point, Nova Scotia

[1] We present a factor analysis-based method for differentiating air masses on the basis of source influence and apply the method to a broad suite of trace gas and aerosol measurements collected at Chebogue Point, Nova Scotia, during the summer of 2004 to characterize the chemical composition of atmospheric outflow from eastern North America. CO, ozone, and aerosol mass were elevated by 30%, 56%, and more than 300% at Chebogue Point during U.S. outflow periods. Organic aerosol mass was highest during U.S. pollution events, but made up the largest fraction (70%) of the total aerosol during periods of primary and especially secondary biogenic influence, indicating the importance of both anthropogenic and biogenic organic aerosol. Anthropogenic and oxygenated volatile organic compounds account for the bulk of the gas-phase organic carbon under most conditions; however, biogenic compounds are important in terms of chemical reactivity. Biogenic emissions thus have a significant impact on the chemistry of air masses downwind of the polluted northeastern United States. Using output from a global 3-D model of atmospheric composition (GEOS-Chem), we estimate that CO directly emitted from U.S. pollution sources makes up 28% of the total CO observed at Chebogue Point during U.S. outflow events and 19% at other times, although more work is needed to improve U.S. emission estimates for CO and other pollutants. We conclude that the effects of North American pollution on the chemistry of the western North Atlantic boundary layer are pervasive and not restricted to particular events.
A Temporal Difference GNG-Based Algorithm That Can Learn to Control in Reinforcement Learning Environments

This paper proposes a new reinforcement learning algorithm called TD-GNG that uses the Growing Neural Gas (GNG) network to deal with environments of large domains. The proposed algorithm is capable to reduce the dimensionality of the problem by aggregating similar states. In experimental comparison against tile-coding in mountain car and puddle world, the TD-GNG showed an increase in the generalization without loosing quality in the policy obtained.
3-D Object Recognition System using Ultrasound

The patterns of ultrasonic reflected echoes from objects contain information about the geometric shape, size, orientation and the surface material properties of the reflector. Accurate estimation of the ultrasonic echo signal pattern is essential for recognition of the target object. We propose a method to classify different objects having specific geometric shape such as cylindrical, rectangular, sphere and conical of different size and material. Here continuous wavelet transform (CWT) has been used for feature extraction. In the present work an attempt has been made to classify the pattern inherent in the features extracted through CWT of different echo signals with the help of two different machine learning algorithms like self organizing feature map (SOFM) and support vector machine (SVM). CWT allows a time domain signal to be transformed into time frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of features from the non-stationary signals like ultrasonic echo envelop. SOFM transforms the input of arbitrary dimension into a one or two dimensional discrete map subject to a topological (neighbourhood preserving) constraint. In the present work the SOFM algorithm with Kohonen's learning and SVM in regression mode has been used to classify the patterns inherent in the features extracted through CWT of different echo envelop
A Study on the Development of Translation Competence: A Perspective of Discourse Construction

The main purpose of this study is to testify the impact of discourse construction on developing students’ translation competence. This paper presented the elements of translation competence based on PATCE model. We used the translation performance of 30 non-English major freshmen for data bank. Through the Corrects a Cool Web, we found that there are four problems in students' translation tasks: lack of cohesion and coherence, default of sentence structure, misuse of words and collocation mistakes. Then we used item analysis and factor analysis to test the relationship between discourse construction and translation competence. These results demonstrate that translation errors occur at every level of language, stemming from different elements of translation competence. And discourse construction penetrates the whole translation process. The outcomes of the study may aid translation teachers in designing course syllabuses by highlighting which elements of translation competence to focus on. Keywords—translation competence; discourse construction; discourse awareness; PACTE model
Swing module scheduling: a lifetime-sensitive approach

This paper presents a novel software pipelining approach, which is called Swing Modulo Scheduling (SMS). It generates schedules that are near optimal in terms of initiation interval, register requirements and stage count. Swing Modulo Scheduling is an heuristic approach that has a low computational cost. The paper describes the technique and evaluates it for the Perfect Club benchmark suite. SMS is compared with other heuristic methods showing that it outperforms them in terms of the quality of the obtained schedules and compilation time. SMS is also compared with an integer linear programming approach that generates optimum schedules but with a huge computational cost, which makes it feasible only for very small loops. For a set of small loops, SMS obtained the optimum initiation interval in all the cases and its schedules required only 5% more registers and a 1% higher stage count than the optimum.
Global-Scale Turbulent Convection and Magnetic Dynamo Action in the Solar Envelope

The operation of the solar global dynamo appears to involve many dynamical elements, including the generation of fields by the intense turbulence of the deep convection zone, the transport of these fields into the tachocline region near the base of the convection zone, the storage and amplification of toroidal fields in the tachocline by differential rotation, and the destabilization and emergence of such fields due to magnetic buoyancy. Self-consistent magnetohydrodynamic (MHD) simulations that realistically incorporate all of these processes are not yet computationally feasible, although some elements can now be studied with reasonable fidelity. Here we consider the manner in which turbulent compressible convection within the bulk of the solar convection zone can generate large-scale magnetic fields through dynamo action. We accomplish this through a series of three-dimensional numerical simulations of MHD convection within rotating spherical shells using our anelastic spherical harmonic (ASH) code on massively parallel supercomputers. Since differential rotation is a key ingredient in all dynamo models, we also examine here the nature of the rotation profiles that can be sustained within the deep convection zone as strong magnetic fields are built and maintained. We find that the convection is able to maintain a solar-like angular velocity profile despite the influence of Maxwell stresses, which tend to oppose Reynolds stresses and thus reduce the latitudinal angular velocity contrast throughout the convection zone. The dynamo-generated magnetic fields exhibit a complex structure and evolution, with radial fields concentrated in downflow lanes and toroidal fields organized into twisted ribbons that are extended in longitude and achieve field strengths of up to 5000 G. The flows and fields exhibit substantial kinetic and magnetic helicity although systematic hemispherical patterns are only apparent in the former. Fluctuating fields dominate the magnetic energy and account for most of the back-reaction on the flow via Lorentz forces. Mean fields are relatively weak and do not exhibit systematic latitudinal propagation or periodic polarity reversals as in the Sun. This may be attributed to the absence of a tachocline, i.e., a penetrative boundary layer between the convection zone and the deeper radiative interior possessing strong rotational shear. The influence of such a layer will await subsequent studies.
Effect of Kolb's Experiential Learning Strategy on Enhancing Pedagogical Skills of Pre-Service Teachers of Secondary School Level.

This study aimed at investigating the effect of Kolb's Experiential Learning Strategy on enhancing the pedagogical skills of pre-service teachers of secondary school level. Kolb's Experiential Learning is a method of acquiring knowledge, skills, and experiences by creating situation to gain first hand experiences. According to Kolb optimal learning takes place when the learner pass through a cycle of experiential learning. It focuses on the learning process of the individual by reflecting upon the experiences provided and takes the students through the whole process in sequence. This study is an experimental study with 2x3 factorial design. Randomly selected 40 pre-service teachers were the samples of the study. Pedagogical skills of pre-service teachers were measured using 'Observation Cum Rating Scale' prepared by the investigator. Data was analysed using inferential statistic ANOVA at 0.05 level of significance. The study revealed that Kolb's experiential learning strategy is significantly effective than the conventional teaching strategy in enhancing the pedagogical skills of pre-service teachers of secondary school level.
On Reducing the Eﬀect of Silhouette Quality on Individual Gait Recogniton: A Feature Fusion Approach

The quality of the extracted gait silhouettes can hinder the performance and practicability of gait recognition algorithms. In this paper, we propose a framework that integrates a feature fusion approach to improve recognition rate under this situation. Speciﬁcally, we ﬁrst generate a dataset containing gait silhouettes with various qualities based on the CASIA Dataset B. We then fuse gallery data with diﬀerent qualities and project data into embedded subspaces. We perform classiﬁcation based on the Euclidean distances between fused gallery features and probe features. Experimental results show that the proposed algorithm can provide important improvements on recognition rate
An Overview of the Optimal Planning, Design, and Conduct of Phase I Studies of New Therapeutics

Phase I clinical trials represent the first step in bringing promising new treatments from the laboratory to the clinic. Although the importance of phase I clinical trials is widely recognized, there is currently no consensus among the scientific, medical, and statistical communities on how best to do these studies in humans. With the advent of targeted therapies, it has become evident that we need to tailor the design of phase I studies for the particular drug class under investigation and any endpoints that are being defined. The National Cancer Institute (NCI) Investigational Drug Steering Committee (IDSC) provides broad external scientific and clinical input on the design and prioritization of early-phase clinical trials with agents for which the NCI Cancer Therapy Evaluation Program (CTEP) holds an Investigational New Drug (IND) application through the U.S. Food and Drug Administration (FDA). The IDSC has formed a number of task forces and working groups, including the Clinical Trial Design Task Force and the Biomarker Working Group, many with membership from within the IDSC as well as external experts, including participants from academia, the pharmaceutical industry, and regulatory authorities. The Clinical Trials Design Taskforce sponsored a Phase I Workshop with the primary goal being to develop consensus recommendations for the optimal design of phase I studies. The primary focus included (1) efficient trial designs, (2) phase I drug combinations, and (3) appropriate statistical and correlative endpoints. In this CCR Focus series, articles summarize key aspects and recommendations on phase I studies (including combination trials), such as design, use of biomarkers, the European Union and Japanese perspectives on design, requirements for first-in-human and other phase I studies, and ensuring regulatory and International Conference on Harmonization (ICH) compliance. A final article summarizes recommendations for the design and conduct of phase II studies. Clin Cancer Res; 16(6); 1710–8
Perceptual Hashing for Color Images Using Invariant Moments

Image hashing is a new technology in multimedia security. It maps visually identical images to the same or similar short strings called image hashes, and finds applications in image retrieval, image authentication, digital watermarking, image indexing, and image copy detection. This paper presents a perceptual hashing for color images. The input image in RGB color space is firstly converted into a normalized image by interpolation and filtering. Color space conversions from RGB to YCbCr and HSI are then performed. Next, invariant moments of each component of the above two color spaces are calculated. The image hash is finally obtained by concatenating the invariant moments of these components. Similarity between image hashes is evaluated by L2 norm. Experiments show that the proposed hashing is robust against normal digital processing, such as JPEG compression, watermark embedding, gamma correction, Gaussian low-pass filtering, adjustments of brightness and contrast, image scaling, and image rotation. Receiver operating characteristics (ROC) comparisons between the proposed hashing and singular value decompositions (SVD) based hashing, also called SVD-SVD hashing, presented by Kozat et al. at the 11th International Conference on Image Processing (ICIP'04) are conducted, and the results indicate that the proposed hashing shows better performances in robustness and discriminative capability than the SVD-SVD hashing.
Stop-probability estimates computed on a large corpus improve Unsupervised Dependency Parsing

Even though the quality of unsupervised dependency parsers grows, they often fail in recognition of very basic dependencies. In this paper, we exploit a prior knowledge of STOP-probabilities (whether a given word has any children in a given direction), which is obtained from a large raw corpus using the reducibility principle. By incorporating this knowledge into Dependency Model with Valence, we managed to considerably outperform the state-of-theart results in terms of average attachment score over 20 treebanks from CoNLL 2006 and 2007 shared tasks.
Examination of the relationship between emotional stability, personality and procrastination tendency on women employees

Objective: Many of the studies that are conducted on the relationship between personality traits and procrastination that were carried out on students and their academic procrastination reported that the distribution of emotional stability scores on procrastination scores were parabolic. In this study, the relationship between personality traits and procrastination, and the distribution of emotional stability factor scores on procrastination scores were examined on working women. Methods: The Procrastination Scale and the Five Factor Personality Inventory Short Form were administered to 512 working women between the ages of 18 to 60 years. Results: The correlation coefficients calculated between total scores of the Procrastination Scale and factor scores were as follows: -0.17 for extra-version, -0.37 for agreeableness, -0.61 for conscientiousness, 0.50 for emotional instability, and -0.28 for openness to experience. Factor scores of the Personality Inventory explained 0.57% of the variance of the procrastination scores. The factors of conscientiousness and emotional instability made the largest contribution in predicting procrastination scores. Except for the extraversion factor scores there were differences in the remaining four groups factor scores when the procrastination scores were considered. Those receiving low procrastination score received the highest score on agreeableness, conscientiousness and openness to experience factors. Those receiving high procrastination score received the lowest total factor scores. For the emotional instability factor, the group scoring low on procrastination received the lowest; the group scoring high on procrastination received the highest factor score. No evidence was obtained about the parabolic distribution of emotional stability factor on the procrastination score. Discussion: Results showed that there was a strong relationship between personality traits and procrastination level in working women. Although there is a strong relationship between emotional stability and procrastination, it is not clear how it occurs.
First Principles Studies of the Effect of Nickel Carbide Catalyst Composition on Carbon Nanotube Growth

Density functional theory calculations were used to investigate the stability of single-walled carbon nanotubes (CNTs) attached to nanoparticles. The total energies and the adhesion energies between the CNTs and the nanoparticles were calculated for systems where the nanoparticles were either pure Ni or Ni carbide. It was found that the adhesion between the CNT and a pure Ni cluster is stronger than between the same CNT and a Ni carbide cluster although the energy difference was small compared to the total adhesion energies. This adhesion strength implies that CNTs are likely to remain attached to both pure Ni and Ni carbide clusters and that either pure Ni or Ni carbide clusters may be docked onto the open CNT ends to achieve continued growth or electronic contacts between CNTs and electrode materials. The system with a CNT attached to a pure Ni cluster was found to be energetically favored compared to a system containing the same CNT attached to a Ni carbide. The difference in total energy implies that .