Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing Mutable Environments

Mutable environments are a proposed addition to objectoriented languages, in which the environment in which a method runs can be changed from the defaults specified in its class. For this purpose, mutable methods are defined, some of which are predefined and are invoked at specific times. Others can be added by a programmer. A mutable method can be overridden by one or more environment objects, in whose scope a method runs. Mutable environments allow concerns for which aspect-oriented programming was developed to be addressed in a relatively simple framework, which is general enough to support wrappers around methods and error handling. This paper presents a first approximation to the idea, with some preliminary evaluation.
Interactive storytelling in virtual environments: building the “Holodeck”

In this paper, we introduce an ongoing research project, which aims at developing an immersive storytelling environment. In such a system, which is inspired from the “Holodeck” device, human users will be able to interact with artificial actors through speech, while sharing the same physical environment. Once on the set, the users will be able to participate in story generation by influencing virtual actors or altering the common environment. An essential aspect is that user involvement will not have to be permanent, and that the users will be allowed to leave the set to watch the story unfold in their absence and to return at a later stage. We discuss some fundamental AI problems with which the implementation will be faced and relate them to narrative theories and models of storytelling. More specifically, we describe how users can interfere with the virtual actor’s plans to influence story generation in terms of interaction
Transaction control expressions for separation of duties

The author describes a model and notation for specifying and enforcing aspects of integrity policies, particularly separation of duties. The key idea is to associate a transaction control expression with each information object. The transaction control expression constrains the pattern in which transactions can be executed on an object. As operations are actually executed the transaction control expressions gets converted to a history. This history serves to enforce separation of duties. Transient objects with a short lifetime are distinguished from persistent objects which are long-lived. Separation of duties is achieved by maintaining a complete history for transient objects but only a partial history for persistent objects. This is possible because of the system-enforced rule that transactions are executed on persistent objects only as a side effect of execution on transient objects. <<ETX>>
Disengaging or Disappearing? Losing the most Socially Motivated Students from Engineering?

Engineering has been marketing itself to high school students as a discipline that helps people. As more socially motivated students enter into engineering, an outstanding question is whether or not these students are retained to graduate in engineering or leave to other disciplines at higher rates as compared to less socially motivated peers. A previous study that characterized students’ motivations toward engineering using interview methods found that female students whose primary motivation toward engineering was to help underserved populations left engineering at a higher rate than female students with other primary motivations toward engineering. The students participating in that study were initially enrolled at four different institutions, but the study population was quite small (n~30). The current research used quantitative methods to characterize the social responsibility (SR) attitudes of a larger population of incoming engineering students at a single institution. A high SR score was defined as an average score across nine 7-point Likert-type items at the third quartile or higher. Among 122 students who entered engineering at the institution in fall 2011, 45% of those with high initial SR scores left engineering, compared to only 28% among students with lower SR scores. Among 57 students initially majoring in mechanical, civil, or environmental engineering in fall 2012, 44% of the students with high SR scores left engineering, compared to only 34% leaving who did not possess high SR scores. Both gender and major appeared to play a role in the results. The curricular context of engineering majors at this institution are discussed in terms of social context, technical/non-technical balance and course flexibility, as compared to the most common destination majors of the students who left engineering. The preliminary results suggest that engineering programs that wish to retain highly socially motivated students should explore the infusion of social context into engineering courses beyond the first year, as well as the required balance of technical and non-technical coursework in their curriculum and opportunities for course choice.
Cross Language Information Retrieval Model for Discovering WSDL Documents Using Arabic Language Query

Web service discovery is the process of finding a suitable Web service for a given user’s query through analyzing the web service‘s WSDL content and finding the best match for the user’s query. The service query should be written in the same language of the WSDL, for example English. Cross Language Information Retrieval techniques does not exist in the web service discovery process. The absence of CLIR methods limits the search language to the English language keywords only, which raises the following question “How do people that do not know the English Language find a web service, This paper proposes the application of CLIR techniques and IR methods to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on WSDL content and user’s query to be ready for CLIR methods. The proposed model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue. org/ and used for solving the research problem with 99.87 % accuracy and 95.06 precision
Are Some Natural Environments More Psychologically Beneficial Than Others? The Importance of Type and Quality on Connectedness to Nature and Psychological Restoration

Exposure to nature can strengthen an individual’s sense of connectedness (i.e., emotional/cognitive bonds to the natural world) and enhance psychological restoration (e.g., feeling relaxed/refreshed). To date, there have been few large studies looking at the role that type and quality of natural environments may have on these outcomes. The present study used data from a large survey in England (sample analyzed = 4,515), which asked participants to recall a recent visit to nature. After controlling for covariates, respondents recalled greater connectedness to nature and restoration following visits to rural and coastal locations compared with urban green space, and to sites of higher environmental quality (operationalized by protected/designated area status, for example, nature reserves). A series of structural equation analyses provided evidence for a bidirectional association between connectedness and restoration. Consideration of the psychological benefits associated with different types and quality of environment has implications for human health, environmental management, and conservation.
Applying Stylometric Analysis Techniques to Counter Anonymity in Cyberspace

Due to the ubiquitous nature and anonymity abuses in cyberspace, it’s difficult to make criminal identity tracing in cybercrime investigation. Writeprint identification offers a valuable tool to counter anonymity by applying stylometric analysis technique to help identify individuals based on textual traces. In this study, a framework for online writeprint identification is proposed. Variable length character n-gram is used to represent the author’s writing style. The technique of IG seeded GA based feature selection for Ensemble (IGAE) is also developed to build an identification model based on individual author level features. Several specific components for dealing with the individual feature set are integrated to improve the performance. The proposed feature and technique are evaluated on a real world data set encompassing reviews posted by 50 Amazon customers. The experimental results show the effectiveness of the proposed framework, with accuracy over 94% for 20 authors and over 80% for 50 ones. Compared with the baseline technique (Support Vector Machine), a higher performance is achieved by using IGAE, resulting in a 2% and 8% improvement over SVM for 20 and 50 authors respectively. Moreover, it has been shown that IGAE is more scalable in terms of the number of authors, than author group level based methods.
Improved hierarchical lossless video coding

Lossy video coding can be achieved by using the H.264 Advanced Video Coding. Some methods have been proposed to transform this coder into a lossless one. The hierarchical lossless video coding structure with new intra algorithm prediction was proposed to reach this goal. In this paper we proposed some enhancements of this method to improve the coding efficiency. The simulation results show that the proposed enhancements reduce the total bit of the coded sequence and the execution time.
Phase space and black-hole entropy of higher genus horizons in loop quantum gravity

In the context of loop quantum gravity, we construct the phase space of isolated horizons with genus greater than 0. Within the loop quantum gravity framework, these horizons are described by genus g surfaces with N punctures and the dimension of the corresponding phase space is calculated including the genus cycles as degrees of freedom. From this, the black-hole entropy can be calculated by counting the microstates which correspond to a black hole of fixed area. We find that the leading term agrees with the A/4 law and that the sub-leading contribution is modified by the genus cycles.
Designing Dependable Digital Domestic Environments

The aim of this paper is to examine the distinctions between home and organizational settings with particular reference to assistive technologies (AT) and outline a model for assessing dependability issues in these environments. For the purposes of this paper we consider assistive technologies to be software-controlled networks of assistive devices. Clearly a home is a personal concept and a social construction, which imbues different meanings to each individual through social actions and the assignment of meaning to those actions. It is therefore important that any method of investigation is sensitive to the changing meanings and nature of people's conceptions of home. This paper outlines the fundamental concepts used by the Lancaster team and proposes a method of conceptualizing dependability within a home context. This paper suggests that the design of AT involves a number of factors that can be derived from a number of sources but essentially all design should place the user at the centre of the process. We aim to show that the home is different from the standard organization and as such deserves consideration in its own right and technology systems need to meet certain criteria within domestic situations that are not covered within traditional organizations. We extend this notion by considering the use of A T in terms of previous models of design and assessment. We also acknowledge that older people are not a homogenous category, and that designing for a group requires sensitivity to the individual needs of the person rather than the categorization of the person. We then consider the role of systems development and deployment from the perspective of designing AT systems for older people and this brings us to consider the problems that are associated with dependability. We contend that standard dependability analysis falls short of the full picture of analysis when applied to domestic settings.
Why children omit function morphemes: Metric vs. syntactic structure

It is an interesting and noticeable fact that in the early stages of language production, children omit certain elements from their speech. Upon closer inspection, it becomes clear that children regularly omit certain function morphemes, as opposed to content words. Function morphemes (including determiners, pronouns, auxiliary verbs, and inflection morphemes) form a natural class from both a phonological and a syntactic perspective: phonologically speaking, in many languages (including English), function morphemes tend to be unstressed and monosyllabic; syntactically speaking, function morphemes are the heads of functional projections, which appear notoriously late in children's speech (Radford 1988). Thus, we can ask whether children omit functional elements for phonological or syntactic reasons. If children omit function morphemes for reasons related to metrical stress, we should find similarities between unstressed function m rphemes and unstressed lexical syllables, and, depending on our assumptions about metric structure, we might find asymmetries in the omission rates of subject vs. object determiners. 1 On the other hand, if children omit function morphemes for syntactic reasons, according to some accounts (e.g. Hoekstra, Hyams & Becker 1997, Clahsen et al. 1996) we should find correlations between omissions in the subject and the predicate, following the structural contingency known as spec-head agreement. In fact we find both of these patterns, which suggests that both metrical and syntactic processes constrain children's output forms, but in different ways.
Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control

Introduction Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution. Methods We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach’s ability to control hand posture and finger forces. Results Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83–99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution. Discussion This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.
Multiobjective optimization with linguistic variables

Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for findig a fair solution to these problems. Suppose we are given a multiobjective mathematical programming problem in which the functional relationship between the decision variables and the objective functions is not completely known. Our knowledge-base consists of a block of fuzzy if-then rules, where the antecedent part of the rules contains some linguistic values of the decision variables, and the consequence part consists of a linguistic value of the objective functions. We suggest the use of Tsukamoto’s fuzzy reasoning method to determine the crisp functional relationship between the decision variables and objective functions. We model the anding of the objective functions by a t-norm and solve the resulting (usually nonlinear) programming problem to find a fair optimal solution to the original fuzzy multiobjective problem.
An All-Digital Delay-Locked Loop Using a New LPF State Machine

An all-digital delay-locked loop (DLL) is presented in this paper. A new variable-step LPF state machine and a variable taps delay line are used here to achieve shorter lock in time than classical digital DLLs. The design is implemented in 0.18 mum CMOS technology. The experiment results show that the all-digital DLL can run at the frequency of about 800 MHz. The power dissipation of the DLL core is about 110 mW under a 1.8 V power supply
Distributed under Creative Commons Cc-by 4.0 a Socratic Epistemology for Verbal Emotional Intelligence

We describe and experimentally validate a question-asking framework for machine-learned linguistic knowledge about human emotions. Using the Socratic method as a theoretical inspiration, we develop an experimental method and computational model for computers to learn subjective information about emotions by playing emotion twenty questions (EMO20Q), a game of twenty questions limited to words denoting emotions. Using human–human EMO20Q data we bootstrap a sequential Bayesian model that drives a generalized pushdown automaton-based dialog agent that further learns from 300 human–computer dialogs collected on Amazon Mechanical Turk. The human–human EMO20Q dialogs show the capability of humans to use a large, rich, subjective vocabulary of emotion words. Training on successive batches of human–computer EMO20Q dialogs shows that the automated agent is able to learn from subsequent human–computer interactions. Our results show that the training procedure enables the agent to learn a large set of emotion words. The fully trained agent successfully completes EMO20Q at 67% of human performance and 30% better than the bootstrapped agent. Even when the agent fails to guess the human opponent's emotion word in the EMO20Q game, the agent's behavior of searching for knowledge makes it appear human-like, which enables the agent to maintain user engagement and learn new, out-of-vocabulary words. These results lead us to conclude that the question-asking methodology and its implementation as a sequential Bayes pushdown automaton are a successful model for the cognitive abilities involved in learning, retrieving, and using emotion words by an automated agent in a dialog setting.
Gaussian Mixture Gain Priors for Regularized Nonnegative Matrix Factorization in Single-Channel Source Separation

We propose a new method to incorporate statistical priors on the solution of the nonnegative matrix factorization (NMF) for single-channel source separation (SCSS) applications. The Gaussian mixture model (GMM) is used as a log-normalized gain prior model for the NMF solution. The normalization makes the prior models energy independent. In NMF based SCSS, NMF is used to decompose the spectra of the observed mixed signal as a weighted linear combination of a set of trained basis vectors. In this work, the NMF decomposition weights are enforced to consider statistical prior information on the weight combination patterns that the trained basis vectors can jointly receive for each source in the observed mixed signal. The NMF solutions for the weights are encouraged to increase the loglikelihood with the trained gain prior GMMs while reducing the NMF reconstruction error at the same time.
Research on Method of Object-Oriented Test Cases Generation Based on UML and LTS

We present a novel method of Object-Oriented test case generation based on UML state diagrams and Label Transition System (LTS). The procedure is based on model-based testing techniques with test cases generated from UML state diagrams translated into LTS. UML state diagram can be a model of modeling software system. LTS model is an internal model to precisely represent the state transition. The examples and analysis show that our method is effective.
The impact of risk and protective factors on message bias

by CAROLYN J. BROOKS (Under the Direction of Jennifer L. Monahan) ABSTRACT This study examined the relationships between seven risk and protective factors and perceptions of bias in anti-smoking messages among an adolescent sample (N = 392; mean age = 14.78 years). Repeated measure analyses were conducted. Parental monitoring was the only protective factor associated with lower perceptions of bias; whereas three of four of the risk factors (number of family/peer smokers, tobacco receptivity, and smoking behavior) were associated with greater perceptions of bias. Both interpersonal variables examined (parental monitoring & number of family/peer smokers), but only two of five individual variables (tobacco receptivity & smoking behavior) were associated with bias. Results indicate that factors external to the self do influence perceptions of message bias, and that those who are a heightened risk for smoking are more likely to perceive messages as biased. Messages should be carefully designed and disseminated to minimize the potential for message bias and rejection.
The PRINTS Database: A Resource for Identification of Protein Families

The PRINTS database houses a collection of protein fingerprints, which may be used to assign family and functional attributes to uncharacterised sequences, such as those currently emanating from the various genome-sequencing projects. The April 2002 release includes 1,700 family fingerprints, encoding approximately 10,500 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Fingerprints are groups of conserved motifs that, taken together, provide diagnostic protein family signatures. They derive much of their potency from the biological context afforded by matching motif neighbours; this makes them at once more flexible and powerful than single-motif approaches. The technique further departs from other pattern-matching methods by readily allowing the creation of fingerprints at superfamily-, family- and subfamily-specific levels, thereby allowing more fine-grained diagnoses. Here, we provide an overview of the method of protein fingerprinting and how the results of fingerprint analyses are used to build PRINTS and its relational cousin, PRINTS-S.
Ear recognition based on wavelet transform and Discriminative Common Vectors

Discriminative common vectors is one of the most successful methods which overcome the small sample size case in Fisher¿s linear discriminant analysis. But when we directly use DCV to reduce the dimensions of the ear images, the computational expense of training is still relatively large. A new method is proposed in this paper that the low frequency sub-images are obtained by utilizing two-dimensional wavelet transform firstly and the features are extracted by applying DCV to the sub-images. The experimental results show that the proposed Wavelet+DCV method achieve better performance than PCA+LDA method and the computation burden is also reduced greatly.
Particle Swarm Optimization-Based Consensus Achievement of a Decentralized Sensor Network

This paper explores the possibility of enhancing the decision-making capability of a decentralized sensor network by establishing cooperative behavior among sensor agents. For that purpose, it was necessary to devise a robust consensus mechanism that distributes sensing information through local fusing with neighbors rather than through a centralized control. This paper proposed a novel particle swarm optimization framework to achieve robust consensus of decentralized sensors; the new framework showed an 16.5% improvement in consensus achievement as compared to the classic majority rule method. Noteworthy enhancements in consensus achievement are also pertinent to the comparable situation of decentralized-sensor network.