Miracle-dz/UniFAR-FTUs huggingface dataset:
Civil Conflict and Displacement: Village-Level Determinants of Forced Migration in Aceh

The purpose of this article is to identify the determinants of displacement behavior based on various push and pull factors at the village level. The study concentrates on changes in village population during three years of civil conflict (1999—2002) in Aceh, Indonesia. The empirical analysis is based on a unique dataset from two census rounds of the Indonesian Village Potential Census (PODES). It uses data on around 5,200 Acehnese villages and relates village-level population change to conflict variables, geographic patterns, and traditional socio-economic determinants of migration. By applying quantile regressions, the push (outflow) factors and the pull (inflow) determinants of migration can also be distinguished. The authors identify the following factors as the main determinants of the Aceh migration pattern in this period. First, conflict clashes induced large rearrangements of the population between villages in highly affected districts, as well as strong village emigration from the geographically remote regions in Central Aceh towards the less conflict-affected coastal industrial areas. Besides conflict factors, an (ongoing) rural—urban migration process, driven by socio-economic factors, has taken place during the conflict period. Second, there is also evidence that security considerations, such as the presence of police in a village or neighborhood, were either emigration-reducing or immigration-inducing. Third, although the presence of ethnic Javanese has not been a primary cause of conflict incidence, their intimidation by the rebel movement has led to a significant outflow, primarily from conflict-affected villages in Central Aceh. These results reveal that, beside a conflict-induced fear of violence, population movements in Aceh have also been an outcome of traditional migration determinants.
Knocking Down the DRD2 by shRNA Expressing Plasmids in the Nucleus Accumbens Prevented the Disrupting Effect of Apomorphine on Prepulse Inhibition in Rat

Prepulse Inhibition (PPI), the objective measure of sensorimotor gating disturbance has being widely used in animal models of schizophrenia. Dopaminergic direct and indirect agonists impair PPI. However, the profile of dopaminergic receptors involved in PPI impairment by dopamine agonists is not clear. By injecting shRNA expressing plasmids against dopamine D2 receptor genes (DRD2) in the nucleus accumbens, here, we studied the effect of apomorphine on PPI in D2 down-regulated rats. Seventy two adult Wistar rats assigned randomly in nine groups, each received coding (250 and 500 ng/µl) or noncoding shRNA expressing plasmids against DRD2 in the nucleus accumbens, with or without apomorphine (0.5 mg/kg, S.C., 72 hours after treatment with plasmids). Auditory startle response and PPI were measured after apomorphine injection. Real time RT-PCR was used to measure DRD2 expression. Results showed that apomorphine significantly decreased PPI in noncoding plasmid treated rats; While, PPI did not impaired in rats pretreated with 250 and 500 ng/µl shRNA expressing plasmids. Accordingly, the expression of DRD2 mRNA in the nucleus accumbens showed 72-78% decrease in expressing plasmid treated rats. Additionally, treatment with expressing plasmids had no effect on basal PPI and/or auditory startle response. Taken together, our results demonstrated that DRD2 silencing in the nucleus accumbens can prevent PPI impairment by apomorphine. These observations suggest application of molecular techniques such as the use of shRNA against DRD2s in studies of schizophrenia pathophysiology and development of new treatments in schizophrenia.
Trespassers will be prosecuted: computer crime in the 1990s

La criminalite informatique est devenue une des priorites du Departement de la Justice aux Etats-Unis. Cette delinquance en col blanc est percue comme une menace nationale d'ou les lois de 1984 et 1986 reprimant les crimes informatiques. Mais rares sont les jugements relevant de ces textes, manifestant ainsi la faiblesse de l'arsenal judiciaire
Socio-Cultural Challenges for the Internet in South Africa

A key aspect of technology policy in South Africa today is universal access. This sounds like a technological problem, but closer examination shows there are significant cultural problems. As simple a concept as an internet cafe, for example, does not translate to a culture where coffee is not popular. One has to examine the essence of the concept of the internet cafe to translate it into something workable in other cultures. Another problem in a multi-ethnic country is language. South Africa has 11 official languages in addition to several others spoken by sizable minorities. Does universal access mean all languages must be accommodated—or does globalization imply English must be forced on everyone? A final complication which makes South Africa interesting is that there is an extremely wide variation across the population in terms of literacy, prior exposure to technology and disposable income. Thus, abilities of individuals to use technology and to pay for it vary widely. This paper examines the cultural and social problems presented by a society as diverse as South Africa in terms of large-scale deployment of the internet.
Intravenous Immunoglobulin Treatment in Chronic Neurological Diseases: Do We Have Maintenance Dose Right?

Objectives. We tried to define, on individual basis, minimal effective maintenance dose of intravenous immunoglobulins (IVIG) in 26 patients with chronic neurological conditions requiring long-term IVIG treatment. Methods. Clinical criteria were reviewed in individual cases (Phase 1) followed by titration phase (Phase 2, 12 months) and posttitration/follow-up phase (Phase 3, 3 months). Objective neurological examination and patient self-reports were used for clinical follow-up. Results. 69.2% of patients reported condition as stable, 26.9% as better, and 3.9% as mildly worse. Original mean monthly dose was 1 g/kg; over the period of 12 months we reduced dose of IVIG to mean dose 0.67 g/kg (range 0.3–2.5 g/kg, P < 0.0001) which meant reduction by 36.4%. We identified 4 nonresponders and diagnosis in one case was reclassified to degenerative disease. In follow-up phase we reduced dose further to 0.60 g/kg. Cumulative monthly dose dropped from 2040 g to 1298 g and to 991 g, respectively. Financial expenses were reduced significantly (by −36.4% during titration phase and by −51.4% during follow-up phase) (comparing with baseline) (P < 0.0001). Conclusion. Individual dose titration leads to significant maintenance IVIG dose reduction with preserved clinical efficacy. Maintenance dose below 1 g/kg (in our study around 0.7 g/kg) has acceptable risk/benefit ratio.
Segmentation and Indexing of Endoscopic Videos

Over the last few years it has become common to archive video recordings of endoscopic surgeries. These videos are of high value for medics, junior doctors, patients and hospital management but currently they are used rarely or not at all. Each day tens to hundreds of hours of new videos are added to archives without metadata that would support content-based search. In order to fully utilize these videos it is necessary to analyze the content of the recordings. Endoscopic videos are in some aspects fundamentally different to other types of videos. Therefore, pre-existing content-based analysis methods must be tested for their ability to operate with this kind of video and, if required, they must be adopted or new methods must be found. Especially, we address video segmentation and indexing in this work. We present our preliminary work and ideas for future work to add content-based information to endoscopic videos.
Traversing Environments Using Possibility Graphs for Humanoid Robots

Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically only designed for one mode of locomotion, but traversing unfavorable environments may require several forms of locomotion to be sequenced together, such as walking, crawling, and jumping. Multi-modal motion planners can be used to address some of these problems, but existing implementations tend to be time-consuming and are limited to quasi-static actions. This paper presents a motion planning method to traverse complex environments using multiple categories of actions. We introduce the concept of the "Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level single-action motion planners to be utilized more efficiently. We show that the Possibility Graph can quickly find paths through several different challenging environments which require various combinations of actions in order to traverse.
A multi-faceted approach towards spam-resistible mail

As checking spam became part of our daily life, unsolicited bulk e-mails (UBE) have become unmanageable and intolerable. Bulk volume of spam e-mails delivering to mail transfer agents (MTAs) is similar to the effect of denial of services (DDoS) attacks as it dramatically reduces the dependability and efficiency of networking systems and e-mail servers. Spam mails may also be used to carry viruses and worms which could significantly affect the availability of computer systems and networks. There have been many solutions proposed to filter spam in the past. Unfortunately there is no silver bullet to deter spammers and eliminate spam mails. That is, in isolation, each of existing spam protection mechanisms has its own advantages and disadvantages. In this paper, we analyze the shortcomings of existing anti-spam solutions and propose a multi-faceted approach using the spam-resistible mail agent (SRMA), which provides the most advantages and the least disadvantages of existing anti-spam solutions. Our experiments show that the proposed SRMA is immune to existing spambots and the prototype proves to be effective, feasible and deployable.
Discovery of the Low-Redshift Optical Afterglow of GRB 011121 and Its Progenitor Supernova SN 2001ke

We present the discovery and follow-up observations of the afterglow of the gamma-ray burst GRB 011121 and its associated supernova SN 2001ke. Images were obtained with the Optical Gravitational Lensing Experiment 1.3 m telescope in BVRI passbands, starting 10.3 hr after the burst. The temporal analysis of our early data indicates a steep decay, independent of wavelength, with Fν ∝ t-1.72±0.05. There is no evidence for a break in the light curve earlier than 2.5 days after the burst. The spectral energy distribution determined from the early broadband photometry is a power law with Fν ∝ ν-0.66±0.13 after correcting for a large reddening. Spectra obtained with the Magellan 6.5 m Baade telescope reveal narrow emission lines from the host galaxy that provide a redshift of z = 0.362 ± 0.001 to the GRB. We also present late R- and J-band observations of the afterglow ~7-17 days after the burst. The late-time photometry shows a large deviation from the initial decline, and our data combined with Hubble Space Telescope photometry provide strong evidence for a supernova peaking about 12 rest-frame days after the GRB. The first spectrum ever obtained of a GRB supernova at cosmological distance revealed a blue continuum. SN 2001ke was more blue near maximum than SN 1998bw and faded more quickly, which demonstrates that a range of properties are possible in supernovae that generate GRBs. The blue color is consistent with a supernova interacting with circumstellar gas, and this progenitor wind is also evident in the optical afterglow. This is the best evidence to date that classical, long GRBs are generated by core-collapse supernovae.
Aspect Component Based Software Engineering1

Component Based Software Engineering (CBSE) and Aspect Oriented Programming (AOP) are two disciplines of software engineering, which have been generating a great deal of interest in recent years. From the CBSE point of view, the building of applications becomes a process of assembling independent and reusable software modules called components. However, the necessary dependencies description among components and its latter implementation causes the appearance of crosscutting, a problem that Aspect Orientation resolves effectively. Aspect Oriented Programming allows programmers to express in a separate form the different aspects that intervene in an application which are composed adequately at a later stage. This paper analyses the problem of crosscutting which is produced during component development, and a component based development extension using Aspect Oriented techniques is proposed. This Component based Software Engineering (CBSE) extension has been named Aspect Component Software Engineering (ACBSE).
Incentives, Computation, and Networks: Limitations and Possibilities of Algorithmic Mechanism Design

In the past decade, a theory of manipulation-robust algorithms has been emerging to address the challenges that frequently occur in strategic environments such as the internet. The theory, known as algorithmic mechanism design, builds on the foundations of classical mechanism design from microeconomics and is based on the idea of incentive compatible protocols. Such protocols achieve system-wide objectives through careful design that ensures it is in every agent's best interest to comply with the protocol. As it turns out, however, implementing incentive compatible protocols as advocated in classical mechanism design theory often necessitates solving intractable problems. To address this, algorithmic mechanism design focuses on designing computationally-feasible incentive compatible approximation algorithms. In the first part of this thesis we show the limitations of algorithmic mechanism design. We introduce a novel class of problems which are approximable in the absence of strategic constraints, and have an optimal incentive compatible solution when no computational constraints are enforced; we show that for this class of problems there is no algorithm with a reasonable approximation ratio that is both computationally feasible and incentive compatible. This settles the central open question in algorithmic mechanism design which, since its inception, has been focused on trying to show the hardness of polynomial time incentive compatibility. In the second part of this thesis we show the possibilities of algorithmic mechanism design. We introduce a novel class of problems where the bottleneck for implementation is the constraint on payments. We show that for a broad class of these problems, there are incentive compatible mechanisms with desirable approximation guarantees that do not require overpayments. By resulting to approximations, this result circumvents well known impossibility results from classical mechanism design theory that deem incentive compatibility to be infeasible under a budget.
A Practical Approach to Align Research with Master's Level Courses

Software Engineering is a discipline in computer sciences that covers different topics ranging from formal methods to practical topics. An essential part of Software Engineering is the organization and the management of software projects. From several studies we know that we master the "craftsmanship", which means coding, but suffer in the organizational topics, i.e. project or process management. Those topics are important to Software Engineering, however, they are rather boring for students, which makes it hard to enthuse them about such topics. During the last years we developed a teaching format that on the one hand covers those high-level and abstract topics and, on the other hand, provides students with the opportunity to have experiences, and lecturers to conduct research. In this paper we present a concept for courses that combines the classic teaching formats lecture, seminar and practical training into a new format that allows for interactive teaching as well as for conducting research. We contribute a blueprint, which can be implemented in further courses. We present experiences made in a first implementation in the area of software process management, and conclude the paper with a discussion and a teaching agenda.
Application of a clustering method on sentiment analysis

This article introduces a novel approach for sentiment analysis – the clustering-based sentiment analysis approach. By applying a TF-IDF weighting method, a voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. The methodology has competitive advantages over the two existing types of approaches: symbolic techniques and supervised learning methods. It is a well-performed, efficient and non-human participating approach to solving sentiment analysis problems.
Extensions to Constraint Dependency Parsing for Spoken

A text-based and spoken language processing framework based on the Constraint Dependency Grammar (CDG) developed by Maruyama [24, 25] is discussed. The scope of CDG is expanded to allow for the analysis of sentences containing lexically ambiguous words, to allow feature analysis in constraints, and to e ciently process multiple sentence candidates that are likely to arise in spoken language processing. The bene ts of the CDG parsing approach are summarized. Additionally, the development of CDG grammars using our grammar tools and parser is discussed. 1
Automatic Discovery of Classification and Estimation Algorithms for Earth- Observation Satellite Imagery

Under NASA’s new Earth Observing System (EOS), satellite imagery is expected to arrive back on Earth at rates of gigabytes/day. Techniques for the extraction of useful information from such massive data streams must be efficient and scalable in order to survive in petabyte archive situations, and they must overcome the opacity inherent in the data by classifying or estimating pixels according to user-specified categories such as crop-type or forest health. We are in the process of applying GP to several related satellite remote sensing (RS) classification and estimation problems in such a way as to surmount the usual obstacles to large-scale exploitation of imagery. The fitness functions used for training are based on how well the discovered programs perform on a set of cases from Landsat Thematic Mapper (TM) imagery. Programs are rated on how well they perform on out-of-training-set samples of cases from the same imagery. We have carried out a number of preliminary experiments on a relatively simple binary classification task. Each case is a set of 7 spectral intensity readings for a pixel and an associated ground truth class: 1 for surface water, 0 for none. The GP system very rapidly discovers simple relations that correctly predict 98%+ for training and testing data sets. The key problem with the results we have observed so far is that the simple solutions rapidly drive out diversity in the population. Several approaches will be taken in further study in order to try to maintain diversity in the population.
Seeing the whole in parts: text summarization for web browsing on handheld devices

3. A process for treating the surface of a substantially non-solvated particulate aluminum hydride which comprises; A. CONTACTING A SUBSTANTIALLY NON-SOLVATED PARTICULATE ALUMINUM HYDRIDE WITH A CYANO GROUP CONTAINING COMPOUND, SAID CYANO GROUP CONTAINING COMPOUND BEING A MEMBER SELECTED FROM THE GROUP CONSISTING ESSENTIALLY OF ALIPHATIC NITRILES, AROMATIC NITRILES, HYRODGEN CYANIDE AND CYANOGEN, AND B. MAINTAINING SAID ALUMINUM HYDRIDE AND SAID CYANO GROUP CONTAINING COMPOUND IN CONTACT FOR A PREDETERMINED PERIOD OF TIME THEREBY TO PROVIDE A SURFACE COATING OF SAID CYANO GROUP CONTAINING COMPOUND ON SAID ALUMINUM HYDRIDE.
Test Construction for Cognitive Diagnosis

Traditional test only provides a total score and computer adaptive test (CAT) not only provides examinee’s score but also estimates examinee’s ability. However, both two methods have not studied students’ knowledge states. Cognitive diagnostic assessment (CDA) is designed to measure specific knowledge structures and processing skills in students so as to provide information about their cognitive strengths and weaknesses. A good diagnostic test is one that goes beyond estimation of individuals’ overall ability levels. How to combine an attribute hierarchy and the reachability matrix with Henson and Douglas’s item selection strategy to construct an effective test for cognitive diagnosis in order to improve the accuracy is discussed in this paper. When compared to randomly constructed tests from the same item bank, classification rates are improved significantly.
Radio resource scheduling and smart antennas in cellular CDMA communication systems

PREFACE. iii ABBREVIATIONS.v CHAPTER ONE: INTRODUCTION . 1 1.1. Network Architecture of 3G mobile communication system.2 1.2. Radio Resource Management (RRM). 4 1.3. Wideband Code Division Multiple Access . 5 1.4. Channel characteristics of mobile radio systems . 6 1.5. Contributions . 8 1.6. Outline of the thesis . 11 CHAPTER TWO: POWER CONTROL ALGORITHMS.12 2.
Der gespielte Ernst des Lebens: Bestandsaufnahme und Potenziale von Serious Games (for Health)

Serious Games werden aktuell im Zusammenhang mit den positiven Potenzialen von Computerspielen in verschiedenen Kontexten diskutiert. Im vorliegenden Beitrag werden die Entwicklung und der Forschungsstand des relativ neuen Konzeptes skizziert und gegenuber anderen Lernkonzepten wie Edutainment, Digital Game-Based Learning und E-learning abgegrenzt. Das Spektrum von Serious Games wird am Beispiel von zwei Spielen aus dem Gesundheitsbereich (Re-Mission und Fatworld) dargestellt und die Potenziale und Grenzen von Serious Games (for Health) erortert. Da bislang nur wenige empirische Befunde zur Wirkung von Serious Games vorliegen, pladieren die Autoren fur eine umfassende Grundlagen- und Wirkungsforschung.
Sparse recovery-based DOA estimator with signal-dependent dictionary

A direction-of-arrival (DOA) estimator based on sparse recovery, where the dictionary entries are formed by discretizing the angle space and correspondingly sampling the received signal. Furthermore, to reduce hardware realization complexity, we use the level-triggered sampling (LTS) to sample the signal at different sensors, which typically samples below the Nyquist rate for bursty signals and needs only 1 bit to represent each sample. The sampled signal vectors and dictionary are then obtained through interpolations. Simulation results show that the proposed method, when compared with existing compressive sensing-based methods, achieves better estimation performance with less samples.
Briefing in Application of Machine Learning Methods in Ion Channel Prediction

In cells, ion channels are one of the most important classes of membrane proteins which allow inorganic ions to move across the membrane. A wide range of biological processes are involved and regulated by the opening and closing of ion channels. Ion channels can be classified into numerous classes and different types of ion channels exhibit different functions. Thus, the correct identification of ion channels and their types using computational methods will provide in-depth insights into their function in various biological processes. In this review, we will briefly introduce and discuss the recent progress in ion channel prediction using machine learning methods.