nils-herrmann/clean_scirepeval huggingface dataset:
Model-Free Imitation Learning with Policy Optimization

In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations. Existing imitation learning algorithms typically involve solving a sequence of planning or reinforcement learning problems. Such algorithms are therefore not directly applicable to large, high-dimensional environments, and their performance can significantly degrade if the planning problems are not solved to optimality. Under the apprenticeship learning formalism, we develop alternative model-free algorithms for finding a parameterized stochastic policy that performs at least as well as an expert policy on an unknown cost function, based on sample trajectories from the expert. Our approach, based on policy gradients, scales to large continuous environments with guaranteed convergence to local minima.
Semi-Supervised Sequence Modeling with Cross-View Training

Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from taskspecific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multitask learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art
Comparing Typical Opening Move Choices Made by Humans and Chess Engines

The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books. For chess, which has a very rich opening theory, large databases of highquality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected. In order to nd out whether the opening books used by modern chess engines in machine versus machine competitions are \comparable" to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans’ games and the other from machines’ games. Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans’ and machines’ choices of opening moves when using a book to guide their choices.
Ee364a: Convex Optimization I Final Exam

This is a 24 hour take-home final. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer programs, but you may not discuss the exam with anyone until August 16, after everyone has taken the exam. The only exception is that you can ask us for clarification, via the course staff email address. We've tried pretty hard to make the exam unambiguous and clear, so we're unlikely to say much. Please make a copy of your exam, or scan it, before handing it in. Please attach the cover page to the front of your exam. Assemble your solutions in order (problem 1, problem 2, problem 3,. . .), starting a new page for each problem. Put everything associated with each problem (e.g., text, code, plots) together; do not attach code or plots at the end of the final. We will deduct points from long, needlessly complex solutions, even if they are correct. Our solutions are not long, so if you find that your solution to a problem goes on and on for many pages, you should try to figure out a simpler one. We expect neat, legible exams from everyone, including those enrolled Cr/N. When a problem involves computation you must give all of the following: a clear discussion and justification of exactly what you did, the source code that produces the result, and the final numerical results or plots. Files containing problem data can be found in the usual place, Please respect the honor code. Although we allow you to work on homework assignments in small groups, you cannot discuss the final with anyone, at least until everyone has taken it. All problems have equal weight. Some are easy. Others, not so much. Be sure you are using the most recent version of CVX, CVXPY, or Convex.jl. Check your email often during the exam, just in case we need to send out an important announcement. Some problems involve applications. But you do not need to know anything about the problem area to solve the problem; the problem statement contains everything you need.
Advances in Swarm and Computational Intelligence

Continuous opinion dynamics optimizer (CODO) is an algorithm based on human collective opinion formation process for solving continuous optimization problems. In this paper, we have studied the impact of topology and introduction of leaders in the society on the optimization performance of CODO. We have introduced three new variants of CODO and studied the efficacy of algorithms on several benchmark functions. Experimentation demonstrates that scale free CODO performs significantly better than all algorithms. Also, the role played by individuals with different degrees during the optimization process is studied.
Computer Security – ESORICS 2017

I believe the single most important reason why we are so helpless against cyber-attackers is that present systems are not supervisable. This opinion is developed in years spent working on network intrusion detection, both as academic and entrepreneur. I believe we need to start writing software and systems that are supervisable by design; in particular, we should do this for embedded devices. In this paper, I present a personal view on the field of intrusion detection, and conclude with some consideration on software design.
Relational memory during infancy: evidence from eye tracking.

Here we report evidence from a new eye-tracking measure of relational memory that suggests that 9-month-old infants can encode memories in terms of the relations among items, a function putatively subserved by the hippocampus. Infants learned about the association between faces that were superimposed on unique scenic backgrounds. During test trials, infants were shown three faces presented on a familiar scene. All three faces were equally familiar; however, one had been presented with the test background earlier. Visual behavior was recorded continuously using a TOBII eye tracker. Infants looked preferentially at the face that matched the test background very early in the trial; however, the time course of this preferential looking effect varied as a function of delay. These results suggest that by 9 months of age infants can form memories that represent the relations among items and maintain them over short delays.
Generative Adversarial Networks Conditioned by Brain Signals

Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have supported the development of image generation techniques able to reach satisfactory levels of realism. Further improvements have been proposed to condition GANs to generate images matching a specific object category or a short text description. In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images. To accomplish this task, we first demonstrate that brain activity EEG signals encode visually-related information that allows us to accurately discriminate between visual object categories and, accordingly, we extract a more compact class-dependent representation of EEG data using recurrent neural networks. Afterwards, we use the learned EEG manifold to condition image generation employing GANs, which, during inference, will read EEG signals and convert them into images. We tested our generative approach using EEG signals recorded from six subjects while looking at images of the aforementioned 40 visual classes. The results show that for classes represented by well-defined visual patterns (e.g., pandas, airplane, etc.), the generated images are realistic and highly resemble those evoking the EEG signals used for conditioning GANs, resulting in an actual reading-the-mind process.
High Voltage Direct Current ( HVDC ) Transmission Systems Technology Review Paper

Beginning with a brief historical perspective on the development of High Voltage Direct Current (HVDC) transmission systems, this paper presents an overview of the status of HVDC systems in the world today. It then reviews the underlying technology of HVDC systems, and discusses the HVDC systems from a design, construction, operation and maintenance points of view. The paper then discusses the recent developments in HVDC technologies. The paper also presents an economic and financial comparison of HVDC system with those of an AC system; and provides a brief review of reference installations of HVDC systems. The paper concludes with a brief set of guidelines for choosing HVDC systems in today’s electricity system development.
A randomized, prospective, comparison study of a mixture of acacia fiber, psyllium fiber, and fructose vs polyethylene glycol 3350 with electrolytes for the treatment of chronic functional constipation in childhood.

To compare the effectiveness of a mixture of acacia fiber, psyllium fiber, and fructose (AFPFF) with polyethylene glycol 3350 combined with electrolytes (PEG+E) in the treatment of children with chronic functional constipation (CFC); and to evaluate the safety and effectiveness of AFPFF in the treatment of children with CFC.



This was a randomized, open label, prospective, controlled, parallel-group study involving 100 children (M/F: 38/62; mean age ± SD: 6.5 ± 2.7 years) who were diagnosed with CFC according to the Rome III Criteria. Children were randomly divided into 2 groups: 50 children received AFPFF (16.8 g daily) and 50 children received PEG+E (0.5 g/kg daily) for 8 weeks. Primary outcome measures were frequency of bowel movements, stool consistency, fecal incontinence, and improvement of other associated gastrointestinal symptoms. Safety was assessed with evaluation of clinical adverse effects and growth measurements.



Compliance rates were 72% for AFPFF and 96% for PEG+E. A significant improvement of constipation was seen in both groups. After 8 weeks, 77.8% of children treated with AFPFF and 83% of children treated with PEG+E had improved (P = .788). Neither PEG+E nor AFPFF caused any clinically significant side effects during the entire course of the study period.



In this randomized study, we did not find any significant difference between the efficacy of AFPFF and PEG+E in the treatment of children with CFC. Both medications were proved to be safe for CFC treatment, but PEG+E was better accepted by children.
REU: RSSI-based rendezvous on the tiny terrestrial robotic platform (TinyTeRP)

The TinyTeRP is a centimeter-scale, modular wheeled robotic platform developed for the study of swarming or collective behavior. This paper presents the use of TinyTeRPs to implement collective recruitment and rendezvous to a fixed location using several RSSI-based gradient ascent algorithms. We also present a redesign of the wheelbased module with tank treads and a wider base, improving the robot’s mobility over uneven terrain and overall robustness. Lastly, we present improvements to the open source C libraries that allow users to easily implement high-level functions and closed-loop control on the TinyTeRP.
Measurement and Analysis of Large-Scale Network File System Workloads

In this paper we present the analysis of two large-scale network file system workloads. We measured CIFS traffic for two enterprise-class file servers deployed in the NetApp data center for a three month period. One file server was used by marketing, sales, and finance departments and the other by the engineering department. Together these systems represent over 22 TB of storage used by over 1500 employees, making this the first ever large-scale study of the CIFS protocol. We analyzed how our network file system workloads compared to those of previous file system trace studies and took an in-depth look at access, usage, and sharing patterns. We found that our workloads were quite different from those previously studied; for example, our analysis found increased read-write file access patterns, decreased read-write ratios, more random file access, and longer file lifetimes. In addition, we found a number of interesting properties regarding file sharing, file re-use, and the access patterns of file types and users, showing that modern file system workload has changed in the past 5–10 years. This change in workload characteristics has implications on the future design of network file systems, which we describe in the paper.
Online Assignment Management: An Evolutionary Tale

The advent of technology in the 1990s was seen as having the potential to revolutionise electronic management of student assignments. While there were advantages and disadvantages, the potential was seen as a necessary part of the future of this aspect of academia. A number of studies (including Dalgarno et al in 2006) identified issues that supported positive aspects of electronic assignment management but consistently identified drawbacks, suggesting that the maximum achievable potential for these processes may have been reached. To confirm the perception that the technology and process are indeed ‘marking time’ a further study was undertaken at the University of South Australia (UniSA). This paper deals with the study of online receipt, assessment and feedback of assessment utilizing UniSA technology referred to as AssignIT. The study identified that students prefer a paperless approach to marking however there are concerns with the nature, timing and quality of feedback. Staff have not embraced all of the potential elements of electronic management of assignments, identified Occupational Health Safety and Welfare issues, and tended to drift back to traditional manual marking processes through a lack of understanding or confidence in their ability to properly use the technology.
Bilateral subthalamic stimulation for Parkinson's disease by using three-dimensional stereotactic magnetic resonance imaging and electrophysiological guidance.

OBJECT
Several methods are used for stereotactically guided implantation of electrodes into the subthalamic nucleus (STN) for continuous high-frequency stimulation in the treatment of Parkinson's disease (PD). The authors present a stereotactic magnetic resonance (MR) method relying on three-dimensional (3D) T1-weighted images for surgical planning and multiplanar T2-weighted images for direct visualization of the STN, coupled with electrophysiological recording and stimulation guidance.



Twelve patients with advanced PD were enrolled in this study of bilateral STN implantation. Both STNs were visible as 3D ovoid biconvex hypointense structures located in the upper mesencephalon. The coordinates of the centers of the STNs were determined with reference to the patient's anterior commissure-posterior commissure line by using a new landmark, the anterior border of the red nucleus. Electrophysiological monitoring through five parallel tracks was performed simultaneously to define the functional target accurately. Microelectrode recording identified high-frequency, spontaneous, movement-related activity and tremor-related cells within the STNs. Acute STN macrostimulation improved contralateral rigidity and akinesia, suppressed tremor when present, and could induce dyskinesias. The central track, which was directed at the predetermined target by using MR imaging, was selected for implantation of 19 of 24 electrodes. No surgical complications were noted.



At evaluation 6 months after surgery, continuous STN stimulation was shown to have improved parkinsonian motor disability by 64% and 78% in the "off' and "on" medication states, respectively. Antiparkinsonian drug treatment was reduced by 70% in 10 patients and withdrawn in two patients. The severity of levodopa-induced dyskinesias was reduced by 83% and motor fluctuations by 88%. Continuous high-frequency stimulation of the STN applied through electrodes implanted with the aid of 3D MR imaging and electrophysiological guidance is a safe and effective therapy for patients suffering from severe, advanced levodopa-responsive PD.
Reduction to the Pole of Magnetic Anomalies Using Analytic Signal

Due to the dipolar nature of the geomagnetic field, magnetic anomalies observed anywhere rather than magnetic poles are asymmetric even when the causative body distribution is symmetric. This property complicates the interpretation of magnetic data. Reduction to the pole (RTP) is a technique that converts magnetic anomaly to symmetrical pattern which would have been observed with vertical magnetization. This technique usually is applied in frequency domain which has some disadvantages such as noise induction, necessity of using fixed inclination and declination throughout survey area and also unknown remanent magnetization that in many cases restrict its applicability. Analytic signal is a suitable quantity that can be calculated either in space or frequency domain and its amplitude is independent to magnetization direction. In this paper, analytic signal has been used as RTP operator and applied on the synthetic magnetic data and on the real magnetic data from an area in Shahrood region of Iran and results compared to conventional RTP operation. Results show that least difference is relevant to the causative body location and then analytic signal can be used as substituent method for conventional RTP.
Learning in Auctions: Regret is Hard, Envy is Easy

An extensive body of recent work studies the welfare guarantees of simple and prevalent combinatorial auction formats, such as selling m items via simultaneous second price auctions (SiSPAs) , , . These guarantees hold even when the auctions are repeatedly executed and the players use no-regret learning algorithms to choose their actions. Unfortunately, off-the-shelf no-regret learning algorithms for these auctions are computationally inefficient as the number of actions available to the players becomes exponential. We show that this obstacle is inevitable: there are no polynomial-time no-regret learning algorithms for SiSPAs, unless RP ⊇ NP, even when the bidders are unit-demand. Our lower bound raises the question of how good outcomes polynomially-bounded bidders may discover in such auctions. To answer this question, we propose a novel concept of learning in auctions, termed "no-envy learning." This notion is founded upon Walrasian equilibrium, and we show that it is both efficiently implementable and results in approximately optimal welfare, even when the bidders have valuations from the broad class of fractionally subadditive (XOS) valuations (assuming demand oracle access to the valuations) or coverage valuations (even without demand oracles). No-envy learning outcomes are a relaxation of no-regret learning outcomes, which maintain their approximate welfare optimality while endowing them with computational tractability. Our positive and negative results extend to several auction formats that have been studied in the literature via the smoothness paradigm. Our positive results for XOS valuations are enabled by a novel Follow-The-Perturbed-Leader algorithm for settings where the number of experts and states of nature are both infinite, and the payoff function of the learner is non-linear. We show that this algorithm has applications outside of auction settings, establishing significant gains in a recent application of no-regret learning in security games. Our efficient learning result for coverage valuations is based on a novel use of convex rounding schemes and a reduction to online convex optimization.
A raspberry Pi controlled cloud based air and sound pollution monitoring system with temperature and humidity sensing

In recent day scenario, the incessant increase in air and sound pollution prove to be an alarming problem. It has become mandatory to control and appropriately monitor the situation so that the required steps to curb the situation can be undertaken. In this project, an IOT-based method to monitor the Air Quality Index and the Noise Intensity of a region, have been proposed. The recommended technology comprises of four modules namely, the Air Quality Index Monitoring Module, the Sound Intensity Detection Module, the Cloud-based Monitoring Module and the Anomaly Notification Module. Firstly, the Air Quality Index is measured considering the presence of the five criteria air pollutants. Then the sound intensity is detected using respective sensor. After that, the Cloud-based Monitoring Module ensures the process of acquiring the data with the help of Wi-fi-module present in Raspberry Pi which fulfils the objective of analysis of information on a periodical basis. Finally, the Anomaly Notification Module alerts the user in case of an undesired condition.
Identifying Usability Issues with an ERP Implementation

Enterprise Resource Planning (ERP) systems hold great promise for integrating business processes and have proven their worth in a variety of organizations. Yet the gains that they have enabled in terms of increased productivity and cost savings are often achieved in the face of daunting usability problems. While one frequently hears anecdotes about the difficulties involved in using ERP systems, there is little documentation of the types of problems typically faced by users. The purpose of this study is to begin addressing this gap by categorizing and describing the usability issues encountered by one division of a Fortune 500 company in the first years of its large-scale ERP implementation. This study also demonstrates the promise of using collaboration theory to evaluate usability characteristics of existing systems and to design new systems. Given the impressive results already achieved by some corporations with these systems, imagine how much more would be possible if understanding how to use them weren’t such an