Miracle-dz/UniFAR-FTUs huggingface dataset:
Hong Kong United Nations International Crime Victim Survey: Final Report of the 2006 Hong Kong UNICVS

This report presents the findings of the 2006 Hong Kong United Nations International Crime Victim survey. This was the first time the UNICVS was conducted in Hong Kong SAR China. For this reason, no trends in crime over time are available but where appropriate, the results are compared with those of other main cities in the developed and the developing world. The report shows crime victimization rates for ten types of common crimes: car theft, theft from car, household burglary and attempted burglary, robbery, personal theft, assault, and sexual victimization. In addition, the report examines non-conventional crimes such as corruption and bribery, and consumer fraud. A unique feature of the Hong Kong UNICVS is a set of questions on cyber victimization. The report also presents information on other topics related to criminal justice such as reporting to the police and the police response to victimization, fear of crime, crime prevention measures, and opinions about police and sentencing. Hong Kong’s residents attitudes to restorative justice and victims’ participation in the justice process are also examined. In 2006, the Hong Kong government conducted its regular Crime Victim Survey. This provides a unique opportunity to compare the results of the CVS and the UNICVS, and assess the ways in which different methodologies impact on the results of crime victimization surveys.
Ensembling Classifiers for Detecting User Intentions behind Web Queries

Discovering user intentions behind Web search queries is key to improving user experience. Usually, this task is seen as a classification problem, in which a sample of annotated user query intentions are provided to a supervised machine learning algorithm or classifier that learns from these examples and then can classify unseen user queries. This article proposes a new approach based on an ensemble of classifiers. The method combines syntactic and semantic features so as to effectively detect user intentions. Different setting experiments show the promise of this linguistically motivated ensembling approach, by reducing the ranking variance of single classifiers across user intentions.
ICUMSA identification of granulated sugar using discrete wavelet transform and colour moments

Classification and identification of granulated sugar in Indonesia were previously done with no quantitative standard. In the production of granulated sugar, several stages and condition produce different kinds of sugar, resulting the need of supervision. Standardisation was designed to follow ICUMSA, a standard based on chemical process. System was designed to identify ICUMSA value of granulated sugar from its image. System was designed as Multi-Level Perceptron Artificial Neural Network with one hidden layer of five neurons using Levenberg-Marquardt algorithm with output trained to follow known ICUMSA values. Colour and textural features were extracted from 180 images of granulated sugar for Artificial Neural Network inputs. Colour moments, Haralick features, and symlet wavelet transform were used as features. After feature reduction, the designed system correctly identified ICUMSA and classified the 6 samples of granulated sugar with 3.623% of error.
Building a Personalized, Auto-Calibrating Eye Tracker from User Interactions

We present PACE, a Personalized, Automatically Calibrating Eye-tracking system that identifies and collects data unobtrusively from user interaction events on standard computing systems without the need for specialized equipment. PACE relies on eye/facial analysis of webcam data based on a set of robust geometric gaze features and a two-layer data validation mechanism to identify good training samples from daily interaction data. The design of the system is founded on an in-depth investigation of the relationship between gaze patterns and interaction cues, and takes into consideration user preferences and habits. The result is an adaptive, data-driven approach that continuously recalibrates, adapts and improves with additional use. Quantitative evaluation on 31 subjects across different interaction behaviors shows that training instances identified by the PACE data collection have higher gaze point-interaction cue consistency than those identified by conventional approaches. An in-situ study using real-life tasks on a diverse set of interactive applications demonstrates that the PACE gaze estimation achieves an average error of 2. 56º, which is comparable to state-of-the-art, but without the need for explicit training or calibration. This demonstrates the effectiveness of both the gaze estimation method and the corresponding data collection mechanism.
Distributed nonconvex optimization over time-varying networks

In this paper we introduce a novel algorithmic framework for non-convex distributed optimization in multi-agent networks with time-varying (nonsymmetric) topology. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to diffuse information: each agent first solves (possibly inexactly) a local convex approximation of the nonconvex original problem, and then performs local averaging operations. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Finally, the framework is applied to a distributed nonlinear regression problem.
Novel Method for Monocular Vision Based Mobile Robot Localization

A robust environment map with 3D spatial natural landmarks that facilitates monocular vision based mobile robot for global localization is built. The highly distinctive multi-dimensional vector descriptors associated with the features extracted through scale invariant feature transform (SIFT) can be robustly matched despite changes in illumination, scale and viewpoint. The landmarks are 3D restructured with the matching image feature pairs obtained through the KD-tree based nearest search approach. Novel RANSAC approach based on generic optimization for global localization is presented. Experiments on the robot Pioneer3 with monocular vision in our real indoor environment show that our method is of high precision
A New Measurement of the Baryonic Fraction Using the Sparse NGC 3258 Group of Galaxies

New X-ray observations of the sparse NGC 3258 group of galaxies made by the ASCA satellite with good spectral and spatial resolution has revealed that this group has a gravitational potential deep enough to prohibit significant mass removal from the system. The baryonic fraction within 240 h−150 kpc is found to be 0.065+ 0.051−0.020 for h50 = 1, where h50 ≡ H0/50 km s-1 Mpc-1, in good agreement with the universal value of 0.05 ± 0.01 predicted by standard big bang nucleosynthesis for a universe with Ω0 = 1 and h50 = 1. Since the deep potential of the NGC 3258 group ensures that all pristine intragroup gas has been retained, the baryonic fraction of the NGC 3258 group is indicative of the universal value. Consequently, it seems premature to rule out a critical universe.
Achievable rates for Tomlinson-Harashima precoding

The maximum achievable information rate of the zero-forcing Tomlinson-Harashima (1971, 1972) precoder (ZF-THP) is given exactly. Bounds are provided for the minimum mean square error (MMSE) THP. The performance of the THP is characterized on an example AWGN channel, and discussed for arbitrary channels.
Generalized logarithmic error and Newton’s method for the th root.

The problem of obtaining optimal starting values for the calculation of integer roots using Newton's method is considered. It has been shown elsewhere that if relative error is used as the measure of goodness of fit, then optimal results are not obtained when the initial approxi- mation is a best fit. Furthermore, if the so-called logarithmic error instead of the relative error is used in the square root case, then a best initial fit is optimal for both errors It is shown here that tor each positive integer m, m > 3, and each negative integer m, there is a certain gen- eralized logarithmic error for which a best initial fit to the mth root is optimal. It is then shown that an optimal fit can be found by just multiplying a best relative error fit by a certain constant. Also, explicit formulas are found for the optimal initial linear fit.
Design and analysis of an improved bitmessage anti-spam mechanism

The BitMessage protocol offers privacy to its anonymous users. It is a completely decentralized messaging system, enabling users to exchange messages preventing accidental eavesdropping - a nice features in the Post-Snowden Internet Era. Not only messages are sent to every node on the network (making it impossible to understand the intended recipient), but their content is encrypted with the intended recipient public key (so that s/he only can decipher it). As these two properties combined might facilitate spamming, a proof-of-work (PoW) mechanism has been designed to mitigate this threat: only messages exhibiting properties of the PoW are forwarded on the network: since PoW is based on computationally heavy cryptographic functions, this slows down the rate at which spammers can introduce unsolicited messages in the network on the one hand, but also makes it harder to send legitimate messages for regular users on the other hand. In this paper, we (i) carry on an analysis of the current PoW mechanism, (ii) propose a very simple, yet very effective, generalization of the formula that decouples spammers vs legitimate users penalty showing that (iii) at the optimum, our proposal halves the harm spammers can do, avoiding by definition any impact for legitimate users.
Designing a Task Analysis Tool Using Jhotdraw and Design Patterns

Reusability is a desired characteristic of any software library, as it can reduce the time to develop a new software system and it can increase productivity. Frameworks and design patterns support reusability in two different ways: the former provides a skeleton for the new software (design and implementation), so the developers do not have to start from scratch and the latter provides solutions to recurrent problems, reducing the time needed to solve problems that might appear. GTATool is a task analysis tool prototype we are developing that support designers to build usable software systems. Us-ability is based on functionality, interaction between the users and the system, and presentation. All these aspects are treated carefully in the task analysis based methods like Groupware Task Analysis (GTA). The GTATool supports the design of software systems based on the GTA method. JHotDraw is a two-dimensional graphics framework for structured drawing editors. As the GTATool should support graphical task tree editing, it seems that JHotDraw is a good solution to start from. It has been developed using design patterns so it has all the advantages the design patterns have. Other design patterns like Composite and Singleton are used again to solve problems in the model layer of the tool. In this paper we present our approach in designing a GTA tool using JHotDraw and design patterns. The advantages and disadvantages we have observed throughout the design process are also presented.
Designing Games for Presence in Consumer Virtual Reality

With virtual reality technologies entering the consumer market this year, it is the task of those producing content for the virtual reality platform to ensure that users have an experience that lives up to expectations. Virtual reality comes with its own strengths and weaknesses, and these must be taken into account when designing applications to produce the best possible experience for interaction. This paper employs a qualitative case study to examine participants in sessions with a virtual reality game prototype utilizing the Oculus Rift. The game prototype was designed to investigate problem areas of virtual reality. Data was then analyzed through thematic analysis, and a preliminary set of game design heuristics specific to virtual reality were created. These heuristics are written so that virtual reality game designers can easily apply them.
Recent advances in simulation optimization: confidence regions for stochastic approximation algorithms

In principle, known central limit theorems for stochastic approximation schemes permit the simulationist to provide confidence regions for both the optimum and optimizer of a stochastic optimization problem that is solved by means of such algorithms. Unfortunately, the covariance structure of the limiting normal distribution depends in a complex way on the problem data. In particular, the covariance matrix depends not only on variance constants but also on even more statistically challenging parameters (e.g. the Hessian of the objective function at the optimizer). In this paper, we describe an approach to producing such confidence regions that avoids the necessity of having to explicitly estimate the covariance structure of the limiting normal distribution. This procedure offers an easy way for the simulationist to provide confidence regions in the stochastic optimization setting.
The impact of corporate social responsibility on employee performance and cost

Purpose - – The purpose of our paper is to empirically examine the conjectures, which prior literature suggests, that employees work more productively in socially responsible companies and employees are willing to work for less when they work for these companies. Design/methodology/approach - – This study uses ordinary least squares regression to examine the relationship between corporate social responsibility (CSR) and employee performance and between CSR and employee cost. Further, 2SLS is used to address the endogeneity issue. Findings - – The results indicate a positive relation between CSR and employee performance, suggesting that employees in socially responsible companies generate better operating performance than their peers in less socially responsible companies. Findings also reveal that socially responsible companies incur higher labor cost. Research limitations/implications - – First, the CSR ratings constructed by KLD Inc. are an approximate measure of CSR performance. Better CSR measures may yield stronger results. Additionally, the sample firms in our study are relatively large firms. Caution needs be exercised when readers generalize these conclusions. Finally, this sample only consists of public firms. Whether these conclusions hold in private firms remains unknown. The above issues can be investigated in future studies. Practical implications - – The findings of our study should interest managers who contemplate engaging in socially responsible activities, investors and financial analysts who assess firm performance and policymakers who design and implement guidelines on CSR programs. Originality/value - – This is the first paper that directly tests the association between CSR and employee performance and cost. Thus, this study contributes to the CSR literature by offering evidence to show a positive effect of CSR on employee performance. It also contributes to the management accounting literature.
A Study of Perceptional Typologies on Computer Based Assessment(CBA): Instructor and Student Perspectives

This study explores and describes different viewpoints on Computer Based Assessment (CBA) by using Q methodology to identify perspectives of students and instructors and classify these into perceptional typologies. Thirty undergraduate students taking CBA courses and fifteen instructors adopting CBA into their curriculum at a university in Korea, were chosen as participants in this study. As a result of the study, firstly, four types of learners were identified and given the following descriptive labels: (I) CBA Dissatisfaction Type, (II) CBA Friendly Type, (III) Adjustment Seeker Type, and (IV) CBA Apprehensive Type. Secondly, three types of instructors were classified and given the following descriptive labels: (A) CBA Preferred Type, (B) Supplementary Need Type, and (C) Yes-But Mixed Type. Educational and pedagogical implications of these results are discussed in the context of CBA course design.
Revisiting Indonesia’s Sources of Economic Growth and Its Projection Towards 2030

This paper revisits Indonesia’s sources of economic growth using the Growth Accounting Framework with education adjusted employment for period 1971-2007. The study estimates contribution of growth in capital stock, human capital and Total Factor Productivity (TFP) during the period before and after the crisis. TFP played positive but minor role in Indonesia’s economic growth before the crisis. Growth in capital stock had been the main driver, attributing between 50-70% of growth. Growth in human capital accounted for another 30%. The pattern of sources of growth has changed substantially post crisis. TFP growth has played a more significant role, whereas capital stock growth has been increasing but at a meager pace. Human capital has consistently contributed about 30% to the overall growth. The roles of capital stock growth, human capital growth and TFP have been on a more equal footing after post-crisis. If this trend persists, it will have profound implication on the driver of Indonesian economy’s growth in the future and its trajectory projection towards 2030.
Direct parametric reconstruction for dynamic [18F]-FDG PET/CT imaging in the body

Abdominal and thoracic [18F]FDG PET/CT imaging is routinely used in clinical practice and drug development but parametric imaging based on full kinetic analysis is rarely used due to noise induced bias and variance in kinetic parameters and analysis is usually restricted to semi-quantitative indices. Direct parametric estimation using 4D image reconstruction can potentially provide parametric maps of reduced bias and variance but little or no evaluation has been done in the body. Previous studies have demonstrated that body [18F]FDG PET/CT imaging can benefit from these methods but analysis was restricted to macroparameters of interest. In work, we implement and apply a recently proposed direct 4-D algorithm using a 2 tissue compartment model on simulated and real oncology [18F]FDG PET/CT data to directly estimate microparameters of interest and assess potential improvements over the traditional post-reconstruction kinetic analysis. Results on the simulated data suggest clear reduction in bias for K1, k2 and blood volume and less for k3. Similarly, variance reduction was observed for K1, k2, k3 and Ki and less for blood volume. Evaluation on patient data demonstrated similar improvements using the direct 4-D method in agreement with simulations.
The fine-tuning problems of particle physics and anthropic mechanisms

Many of the classic problems of particle physics appear in a very different light when viewed from the perspective of the multiverse. Most importantly the two great ``fine tuning'' problems that motivate the field are far less serious when one accounts for the required anthropic constraints which exist in a multiverse. However, the challenge then becomes to construct a realistic physical theory of the multiverse and test it. I describe some phenomenology of the quark and lepton masses that may provide a window to the theory that underlies the multiverse.
Program Context Specific Buffer Caching with AMP

We present Adaptive Multi-Policy buffer caching (AMP), which uses multiple caching policies within one application, and adapts both which policies to use and their relative fraction of the cache. AMP differentiates disk requests based on the program contexts that issue them, and detects access pattern for each program context. This approach leads to detected patterns more stable than previous approaches that do detection based on processes and files. Compared to recent work, AMP is unique in that it employs a novel robust scheme for detecting looping patterns in access streams, as well as a low-overhead randomized way of managing many cache partitions. We show that AMP outperforms non-detection-based caching algorithms on a variety of workloads. For example, AMP reduces cache miss rate by up to 50% on a large database workloads. Other applications show about 20%-30% reduction in cache miss rate. Compared to other detection-based schemes, AMP detects access patterns more accurately in our experiment with a series of synthesized workloads, and incurs up to 15% fewer misses for one of the application traces. Experience with a Linux implementation is reported. Our prototype shortens the run time of a large database workload by 9.6%.
Genetic Transformation of Cells using Physical Methods

The production of transgenic cells is a routinely process that allows to insert genes from plants, fungi, viruses, bacteria and even animals into cells. Genetic transformation requires penetration of the transgene through the cell wall. This process is facilitated by biological, chemical or physical methods. We present a short review of the state of the art of physical methods used for genetic transformation. A general panorama of the traditional physical genetic transformation methods like electroporation, biolistic, and agitation with glass beads, vacuum infiltration, silicon carbide whisker, laser microbeams, ultrasound, and the newly promising technique of shock wave-mediated genetic transformation of cells are described. These techniques have been applied to transform cells of bacteria, algae, plants, fungi, and animals. In human cells, genetic transformation is currently used for DNA vaccines, tissue engineering, and cancer therapy.
Potent Anti-Tumor Effect Generated by a Novel Human Papillomavirus (HPV) Antagonist Peptide Reactivating the pRb/E2F Pathway

Human papillomavirus type 16 (HPV16) E7 is a viral oncoprotein believed to play a major role in cervical cancer. In this study, an antagonist peptide against HPV16E7 protein was first identified from screening the c7c phage display peptide library. The binding specificity and affinity of the selected peptide to HPV16E7 were tested by competitive enzyme-linked immunosorbent assay (ELISA). The antagonist peptide showed obvious anti-tumor efficacy both in cell lines and animal tumor models. Significant cell proliferation inhibition with high specificity was noted when HPV16-positive cells were treated with the peptide. This anti-tumor efficacy was resulted from overriding the activities of HPV16E7 and reactivating the pRb/E2F pathway, as shown by a series of experiments. Flow cytometry analysis revealed that the selected peptide induced G1 arrest in a dose-dependent manner. Competitive ELISA, pull down, and Co-IP experiments indicated that the selected peptide disrupted the interaction between HPV16E7 and pRb proteins both in vitro and in vivo. Luciferase reporter assay verified that transcription activities of E2F were suppressed by the peptide through restoration of pRb. RT-PCR and Western blot revealed that it reduced cyclins A, D1, and E1 expression, and led to HPV16E7 protein degradation, but pRb protein stabilization. The current study suggests that this specific peptide may serve as a potential therapeutic agent for HPV16-positive cervical cancer.
Distributed streaming reconstruction of information diffusion: poster

Recent advances in social media have triggered a massive engagement of user population: a large part of people's lives has shifted to social media platforms and real events are reported while they are happening (e.g. in Twitter). As a result, such platforms have become an important source of information, being used by professionals as well, e.g. journalists, for fast access to news and events. Social media maintain an underlying network of social connections over which such information propagates. Information diffusion in social media has attracted attention, by analyzing how information is propagated from user to user and who is influenced by whom. Given the scale and speed of such information, systems that can keep up with such fast rates are required. In this poster, we present a system for real time reconstruction of information diffusion that encompass the challenges of analyzing fast data streams combined with large social graphs.