nils-herrmann/clean_scirepeval huggingface dataset:
Text-based adventures of the golovin AI agent

The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books and decompiled games) to create language models suitable to this domain. Moreover, we embed mechanisms that allow us to specify, and separately handle, important tasks as fighting opponents, managing inventory, and navigating on the game map. We validated usefulness of these mechanisms, measuring agent's performance on the set of 50 interactive fiction games. Finally, we show that our agent plays on a level comparable to the winner of the last year Text-Based Adventure AI Competition.
From Single to Dual Robotic Therapy: A Review of the Development Process of iPAM

Stroke is a common condition in the UK with over 30,000 people per annum are left with significant disability. When severe arm paresis is occurs, functional recovery of the affected arm is often poor. This can have a major impact on physical independence. A contributing factor to the poor recovery relates to lack of intensity of appropriate arm exercise practice. Through the use of robotic technology it may be possible to augment the physical therapist's efforts and maximize the impact of the limited physical therapy resource. At Leeds University a prototype single robot system has been developed that is capable of moving a patient's arm through a set of movements prescribed by a physiotherapist. This system is robust to changes in the biomechanical and physiological properties through a large range of patients. Further development of the system has led to a dual robot system that provides a greater level of control over upper-arm joint co-ordination while offering additional support to the shoulder complex. In order to test the systems safely, two models of the human arm have been produced: 1) a 6 degree of freedom computational model simulating how the robot system could provide therapy to a patient. 2) A mechanical model (Derryck), representing the biomechanical properties of the shoulder and elbow. The prototype single robot system is shown to be capable of administering trajectory following (for Derryck) for a large range of arm parameters
Humanoid locomotion on uneven terrain using the time-varying divergent component of motion

This paper presents a framework for dynamic humanoid locomotion on uneven terrain using a novel time-varying extension to the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic CoM height trajectories during stepping. The proposed planning algorithm computes admissible DCM reference trajectories given desired ZMP plans for single and double support. This is accomplished using reverse-time integration of the discretized DCM dynamics over a finite time horizon. To account for discontinuities during replanning, linear Model Predictive Control (MPC) is implemented over a short preview window. DCM tracking control is achieved using a time-varying proportional-integral controller based on the Virtual Repellent Point (VRP). The effectiveness of the combined approach is verified in simulation using a 30-DoF model of THOR, a compliant torque-controlled humanoid.
Sparse Probabilistic Matrix Factorization by Laplace Distribution for Collaborative Filtering

In recommendation systems, probabilistic matrix factorization (PMF) is a state-of-the-art collaborative filtering method by determining the latent features to represent users and items. However, two major issues limiting the usefulness of PMF are the sparsity problem and long-tail distribution. Sparsity refers to the situation that the observed rating data are sparse, which results in that only part of latent features are informative for describing each item/user. Long tail distribution implies that a large fraction of items have few ratings. In this work, we propose a sparse probabilistic matrix factorization method (SPMF) by utilizing a Laplacian distribution to model the item/user factor vector. Laplacian distribution has ability to generate sparse coding, which is beneficial for SPMF to distinguish the relevant and irrelevant latent features with respect to each item/user. Meanwhile, the tails in Laplacian distribution are comparatively heavy, which is rewarding for SPMF to recommend the tail items. Furthermore, a distributed Gibbs sampling algorithm is developed to efficiently train the proposed sparse probabilistic model. A series of experiments on Netflix and Movielens datasets have been conducted to demonstrate that SPMF outperforms the existing PMF and its extended version Bayesian PMF (BPMF), especially for the recommendation of tail items.
Key success factors for implementing Business Intelligence in South African public sector organisations

Business Intelligence (BI) has been rated as a key application and technology investment which provides organisations with great value by improving their decision making processes. The public sector provides a case for implementing BI for improved decision making processes as a way of enhancing its service delivery. However, the implementation of BI in these organisations have revealed to be quite a complex task to undertake. This research paper sets out to explore the implementation of BI in the public sector in South Africa. The research was conducted through two case studies and data was collected by conducting semi-structured interviews and document collection with organisations that are implementing BI. A qualitative thematic analysis method was then used to construct the major themes that emerged from the data. The study revealed that BI can be used as an enabler of change and improvement in public sector activities. Consolidating structures, systems and processes was identified as a precursor to implementing it, while the use of the BI initiative to support organisational strategic objectives was seen as ensuring executive buy-in. However, the level of skills needed to use BI tools was highlighted as key factor in hindering its use in these organisations. ICT has furthermore more been identified as being an important factor for the promotion of development and equitable access to public services.
What makes things fun to learn? heuristics for designing instructional computer games

In this paper, I will describe my intuitions about what makes computer games fun. More detailed descriptions of the experiments and the theory on which this paper is based are given by Malone (1980a, 1980b). My primary goal here is to provide a set of heuristics or guidelines for designers of instructional computer games. I have articulated and organized common sense principles to spark the creativity of instructional designers (see Banet, 1979, for an unstructured list of similar principles). To demonstrate the usefulness of these principles, I have included several applications to actual or proposed instructional games. Throughout the paper I emphasize games with educational uses, but I focus on what makes the games fun, not on what makes them educational.
 Though I will not emphasize the point in this paper, these same ideas can be applied to other educational environments and life situations. In a sense, the categories I will describe constitute a general taxonomy of intrinsic motivation—of what makes an activity fun or rewarding for its own sake rather than for the sake of some external reward (See Lepper and Greene, 1979).
 I think the essential characteristics of good computer games and other intrinsically enjoyable situations can be organized into three categories: challenge, fantasy, and curiosity.
The skin whitening industry in the Philippines.

Skin whitening is a big and booming industry in many developing countries. Its popularity owes mainly to post-colonial, internalized racism. This study examines whether government intervention is necessary and more efficient than market-driven approaches in addressing the health risks and harms associated with skin whitening. We gathered empirical data on the quality and quantity of health-related information about skin whiteners with a multi-stage probability sample of consumers in two cities in the Philippines (n=110; α=0.05). Regardless of their socio-demographic characteristics, we find that cognitive biases and information asymmetries build and sustain consumers' trust in manufacturers and distributors of skin whiteners while, paradoxically, breeding uncertainties over the integrity of these products. The results are product adulteration and misbranding, leading to pricing advantages for toxic whiteners over safer products. This has impeded regulatory efficacy. We recommend anchoring government intervention in transaction cost-reduction, containing the externalities of skin bleaching, and institutionalizing third party partnerships. Failure to do so would leave consumers extremely vulnerable to the forces of supply and demand that favor toxic whiteners, particularly in a market where voluntary collective action is difficult to organize.
Extreme scale with full SQL language support in microsoft SQL Azure

Cloud SQL Server is an Internet scale relational database service which is currently used by Microsoft delivered services and also offered directly as a fully relational database service known as "SQL Azure". One of the principle design objectives in Cloud SQL Server was to provide true SQL support with full ACID transactions within controlled scale "consistency domains" and provide a relaxed degree of consistency across consistency domains that would be viable to clusters of 1,000's of nodes. In this paper, we describe the implementation of Cloud SQL Server with an emphasis on this core design principle.
Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD.

Attention deficit hyperactivity disorder (ADHD) comprises a deficit in behavioral inhibition. A theoretical model is constructed that links inhibition to 4 executive neuropsychological functions that appear to depend on it for their effective execution: (a) working memory, (b) self-regulation of affect-motivation-arousal, (c) internalization of speech, and (d) reconstitution (behavioral analysis and synthesis). Extended to ADHD, the model predicts that ADHD should be associated with secondary impairments in these 4 executive abilities and the motor control they afford. The author reviews evidence for each of these domains of functioning and finds it to be strongest for deficits in behavioral inhibition, working memory, regulation of motivation, and motor control in those with ADHD. Although the model is promising as a potential theory of self-control and ADHD, far more research is required to evaluate its merits and the many predictions it makes about ADHD.
MORPHOLOGICAL CLASSIFICATION OF GALAXIES INTO SPIRALS AND NON-SPIRALS

The aim of this master's thesis is the classification of images of galaxies according to their morphological features using computer vision and artificial intelligence techniques. We deal specifically with the shape of the galaxy in this project. The galaxies are broadly categorized into 3 categories according to their shape: circular, elliptical and spiral. Out of these 3 possible shapes, correctly classifying the spiral shape is the most challenging. This is mostly due to the noisy images of the galaxies and partly due to the shape itself, as spiral can easily be mistaken for an ellipse or even a circle. Thus we focus on classifying the images into only 2 categories: spiral and non-spiral. The first phase of the thesis addresses the process of feature extraction from images of the galaxies, and the second phase uses artificial intelligence and machine learning methods to create a system that categorizes galaxies based on the extracted features. The specific methods used for classification are boosting, logistic regression and deep neural networks. We evaluate these techniques on data from the Galaxy Zoo project  that is freely available to anyone. The languages used are C++ (OpenCV) and Python.
Learning user tastes: a first step to generating healthy meal plans

Poor nutrition is fast becoming one of the major causes of ill-health and death in the western world. It is caused by a variety of factors including lack of nutritional understanding leading to poor choices being made when selecting which dishes to cook and eat. We wish to build systems which can recommend nutritious meal plans to users, however a crucial pre-requisite is to be able to recommend dishes that people will like. In this work we investigate key factors contributing to how recipes are rated by analysing the results of a longterm study (n=123 users) in order to understand how best to approach the recommendation problem. In doing so we identify a number of important contextual factors which can influence the choice of rating and suggest how these might be exploited to build more accurate recipe recommender systems. We see this as a crucial first step in a healthy meal recommender. We conclude by summarising our thoughts on how we will combine recommended recipes into meal plans based on nutritional guidelines.
Automated Transplantation and Differential Testing for Clones

Code clones are common in software. When applying similar edits to clones, developers often find it difficult to examine the runtime behavior of clones. The problem is exacerbated when some clones are tested, while their counterparts are not. To reuse tests for similar but not identical clones, Grafter transplants one clone to its counterpart by (1) identifying variations in identifier names, types, and method call targets, (2) resolving compilation errors caused by such variations through code transformation, and (3) inserting stub code to transfer input data and intermediate output values for examination. To help developers examine behavioral differences between clones, Grafter supports fine-grained differential testing at both the test outcome level and the intermediate program state level. In our evaluation on three open source projects, Grafter successfully reuses tests in 94% of clone pairs without inducing build errors, demonstrating its automated code transplantation capability. To examine the robustness of G RAFTER, we systematically inject faults using a mutation testing tool, Major, and detect behavioral differences induced by seeded faults. Compared with a static cloning bug finder, Grafter detects 31% more mutants using the test-level comparison and almost 2X more using the state-level comparison. This result indicates that Grafter should effectively complement static cloning bug finders.
Pedestrian Detection from a Moving Vehicle

This paper presents a prototype system for pedestrian detection on-board a moving vehicle. The system uses a generic two-step approach for efficient object detection. In the first step, contour features are used in a hierarchical template matching approach to efficiently ”lock” onto candidate solutions. Shape matching is based on Distance Transforms. By capturing the objects shape variability by means of a template hierarchy and using a combined coarse-to-fine approach in shape and parameter space, this method achieves very large speed-ups compared to a brute-force method. We have measured gains of several orders of magnitude. The second step utilizes the richer set of intensity features in a pattern classification approach to verify the candidate solutions (i.e. using Radial Basis Functions). We present experimental results on pedestrian detection off-line and on-board our Urban Traffic Assistant vehicle and discuss the challenges that lie ahead.
iPCA : An Interactive System for PCA-based Visual Analytics

Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a “black box” operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT’s Interactive Data Analysis. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants’ accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.
A 120-mV input, fully integrated dual-mode charge pump in 65-nm CMOS for thermoelectric energy harvester

In this paper, a fully integrated low voltage charge pump for thermoelectric energy harvesters is presented. The proposed dual-mode architecture achieves both the low startup voltage in a startup mode and high conversion efficiency in a normal operation mode without off-chip inductors and capacitors. In the measurement, the proposed circuit successfully converts 120-mV input to 770-mV output with 38.8% conversion efficiency.
An Improved Dijkstra Algorithm for Firefighting

In this paper, briefly the efforts going toward a mathematical calculation to validate a new-Dijkstra algorithm from the original normal Dijkstra algorithm  as an improvised Dijkstra. The result of this improvised Dijkstra was used to find the shortest path for firefighting unit to reach the location of the fire exactly. The idea depends on the influence of turns on the path, while there are two equal paths’, the more number of turns take more time to pass through it and the less number turns takes less time to pass through it. To apply this scenario practically we take a small real area in south Khartoum. The result gives strong justification that proves and verifies our methodology with a clear contribution in Improved Dijkstra algorithm for firefighting like Geo-Dijkstra. Furthermore, an evaluation of the above-mentioned algorithms has been done showing very promising and realistic results.
Medication intake adherence with real time activity recognition on IoT

Usefulness of health care services is seriously effected by medication adherence. Medication intake is one of the cases where adherence may be difficult for the patients, who are willing to undertake the prescribed therapy. Internet of Things (IoT) infrastructure for activity monitoring is a strong candidate solution to maintain adherence of forgetful patients. In this study, we propose an IoT framework where medication intake is ensured with real time continuous activity recognition. We present our use case with the focus of application and network layers. We utilize an activity classification scheme, which considers inter-activity detection consistency based on non-predefined feature extraction as the application layer. The network layer includes a gateway structure ensuring end-to-end reliability in the connection between a wireless sensor network (WSN) and Internet. Results obtained in simulation environment suggest that the selected application and network layers introduce a feasible solution for the medication intake use case.
Random projections fuzzy k-nearest neighbor(RPFKNN) for big data classification

As the number of features in pattern recognition applications continuously grows, new algorithms are necessary to reduce the dimensionality of the feature space while producing comparable results. For example, a dynamic area of research, activity recognition, produces large quantities of high-velocity, high-dimensionality data that require real time classification. While dimensionality reduction approaches such as principle component analysis (PCA) and feature selection work well for datasets of reasonable size and dimensionality, they fail on big data. A possible approach to classification of high-dimensionality datasets is to combine a typical classifier, fuzzy k-nearest neighbor in our case (FKNN), with feature reduction by random projection (RP). As opposed to PCA where one projection matrix is computed based on least square optimality, in RP, a projection matrix is chosen at random multiple times. As the random projection procedure is repeated many times, the question is how to aggregate the values of the classifier obtained in each projection. In this paper we present a fusion strategy for RP FKNN, denoted as RPFKNN. The fusion strategy is based on the class membership values produced by FKNN and classification accuracy in each projection. We test RPFKNN on several synthetic and activity recognition datasets.
Sensor fusion in upper limb area networks: A survey

Body sensor networks (BSNs) have been increasingly used in medical applications such as exoskeleton control, powered prosthesis control, tremor suppression, gesture and sign language recognition systems, and human computer interfaces. This review explores the use of multi-modal sensor fusion in BSNs for the detection, measurement and classification of upper limb for the control of dynamic systems. Specifically, the review will look into the most common multi-modal sensor combinations found in literature, namely inertial measurement units (IMUs) with electromyography (EMG), IMUs with camera systems, EMG with electroencephalography (EEG), and IMUs with flexible force sensors. The advantages and challenges associated with these sensor combinations is discussed, as well as the challenges of sensor fusion in a broad nature, with particular focus on the use of data, feature, or decision level fusion.
Sparse Dueling Bandits

The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a new approach for finding the “best” arm according to the Borda criterion using noisy comparisons. We prove that in the absence of structural assumptions, the sample complexity of this problem is proportional to the sum of the inverse squared gaps between the Borda scores of each suboptimal arm and the best arm. We explore this dependence further and consider structural constraints on the pairwise comparison matrix (a particular form of sparsity natural to this problem) that can significantly reduce the sample complexity. This motivates a new algorithm called Successive Elimination with Comparison Sparsity (SECS) that exploits sparsity to find the Borda winner using fewer samples than standard algorithms. We also evaluate the new algorithm experimentally with synthetic and real data. The results show that the sparsity model and the new algorithm can provide significant improvements over standard approaches.