nils-herrmann/clean_scirepeval huggingface dataset:
Let's talk about it: evaluating contributions through discussion in GitHub

Open source software projects often rely on code contributions from a wide variety of developers to extend the capabilities of their software. Project members evaluate these contributions and often engage in extended discussions to decide whether to integrate changes. These discussions have important implications for project management regarding new contributors and evolution of project requirements and direction. We present a study of how developers in open work environments evaluate and discuss pull requests, a primary method of contribution in GitHub, analyzing a sample of extended discussions around pull requests and interviews with GitHub developers. We found that developers raised issues around contributions over both the appropriateness of the problem that the submitter attempted to solve and the correctness of the implemented solution. Both core project members and third-party stakeholders discussed and sometimes implemented alternative solutions to address these issues. Different stakeholders also influenced the outcome of the evaluation by eliciting support from different communities such as dependent projects or even companies. We also found that evaluation outcomes may be more complex than simply acceptance or rejection. In some cases, although a submitter's contribution was rejected, the core team fulfilled the submitter's technical goals by implementing an alternative solution. We found that the level of a submitter's prior interaction on a project changed how politely developers discussed the contribution and the nature of proposed alternative solutions.
Quantum Reinforcement Learning

The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.
Facial Micro-Expression Detection in Hi-Speed Video Based on Facial Action Coding System (FACS)

Facial micro-expressions are fast and subtle facial motions that are considered as one of the most useful external signs for detecting hidden emotional changes in a person. However, they are not easy to detect and measure as they appear only for a short time, with small muscle contraction in the facial areas where salient features are not available. We propose a new computer vision method for detecting and measuring timing characteristics of facial micro-expressions. The core of this method is based on a descriptor that combines pre-processing masks, histograms and concatenation of spatial-temporal gradient vectors. Presented 3D gradient histogram descriptor is able to detect and measure the timing characteristics of the fast and subtle changes of the facial skin surface. This method is specifically designed for analysis of videos recorded using a hi-speed 200 fps camera. Final classification of micro expressions is done by using a k-mean classifier and a voting procedure. The Facial Action Coding System was utilized to annotate the appearance and dynamics of the expressions in our new hi-speed micro-expressions video database. The efficiency of the proposed approach was validated using our new hi-speed video database. key words: facial motion analysis, high speed camera, video descriptor
High-Performance Four-Stage CMOS OTA Suitable for Large Capacitive Loads

Designing four-stage operational transconductance amplifiers (OTAs) is often considered a challenging task mainly because of the required non trivial frequency compensation procedure. In this study, starting from the simplest architecture (a differential stage cascaded by three common source stages) a high-performance OTA is demonstrated, outperforming all the (few) previous implementations. Thanks to the frequency compensation scheme and to the adoption of a slew-rate enhancer section, the solution is able to drive large capacitive loads as high as 1 nF, rivaling in terms of bandwidth and speed even with three-stage counterparts, traditionally credited to be better because of the less number of high-impedance nodes. The solution, fabricated in a 0.35-μm technology, occupies 0.014-mm2 with DC consumption of 170 μW. It also achieves nearly 3-MHz gain bandwidth product while driving the 1-nF load with less than 0.5-μs 1% settling time.
CAPTCHA Design: Color, Usability, and Security

Most user interfaces use color, which can greatly enhance their design. Because the use of color is typically a usability issue, it rarely causes security failures. However, using color when designing CAPTCHAs, a standard security technology that many commercial websites apply widely, can have an impact on usability and interesting but critical implications for security. Here, the authors examine some CAPTCHAs to determine whether their use of color negatively affects their usability, security, or both.
The Influence of Context on Dialogue Act Recognition

This article presents a deep analysis of the influence of context information on dialogue act recognition. We performed experiments on the annotated subsets of three different corpora: the widely explored Switchboard Dialog Act Corpus, as well as the unexplored LEGO and Cambridge Restaurant corpora. In contrast with previous work, especially in what concerns the Switchboard corpus, we used an event-based classification approach, using Support Vector Machines (SVMs), instead of the more common sequential approaches, such as Hidden Markov Models (HMMs). We opted for such an approach so that we could control the amount of provided context information and, thus, explore its range of influence. Our base features consist of n-grams, punctuation, and whwords. Context information is obtained from previous utterances and provided in three ways – n-grams, n-grams tagged with relative position, and dialogue act classifications. A comparative study was conducted to evaluate the performance of the three approaches. From it, we were able to assess the importance of context information on dialogue act recognition, as well as its range of influence for each of the three selected representations. In addition to the conclusions originated by the analysis, this work also produced results that advance the state-of-the-art, especially considering previous work on the Switchboard corpus. Furthermore, since, to our knowledge, the Email addresses: eugenio.ribeiro@l2f.inesc-id.pt (Eugénio Ribeiro), ricardo.ribeiro@inesc-id.pt (Ricardo Ribeiro), david.matos@inesc-id.pt (David Martins de Matos)  2015 ar X iv :1 50 6. 00 83 9v 1 [ cs .C L ] 2 J un 2 01 5 remaining datasets had not been previously explored for this task, our experiments can be used as baselines for future work on those corpora.
An algorithmic approach to multimodality midfacial rejuvenation using a new classification system for midfacial aging.

Midfacial aging is the result of the complex interplay between the osseous skeleton, facial retaining ligaments, soft tissues envelope, facial fat compartments, and the overlying skin elasticity. As a result of the many anatomic components involved in midfacial aging, the authors proposed a classification system based on distinct anatomic factors to direct surgical treatment. Evidence based data suggest that midface rejuvenation often requires a multimodality approach to obtain desired results, especially in patients with more advanced aging and poor tissue elasticity, or those with hypoplastic midfacial skeletal structure.
Clustering and Community Detection in Directed Networks: A Survey

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed – in the sense that there is directionality on the edges, making the semantics of the edges non symmetric as the source node transmits some property to the target one but not vice versa. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of relevant application domains. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs – with clustering being the primary method sought and the primary tool for community detection and evaluation. The goal of this paper is to offer an in-depth comparative review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.
Adversarial Reinforcement Learning in a Cyber Security Simulation

This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo learning with the Softmax exploration strategy is most effective in performing the defender role and also for learning attacking strategies.
IoT Based Smart Agricultural Device Controlling System

Agriculture is the key to the development of human civilization. In developing and underdeveloped countries most of the populations depend on agriculture. There are many challenges faced by the farmers in agriculture like getting expert advice, monitoring of crops and manual methods of doing the necessary activities required for farming like irrigation etc. Recently, some web-based models have been developed to assist the farmer in agriculture but they are not very effective due to complex interface and lack of technical knowledge of the farmer. In this paper, we propose an IoT based smart agricultural device controlling system to help the farmer effective agriculture. We explore the integration of current environment and agriculture production to the farmer in a simple way through the Arduino mega and GSM module. In the proposed system the farmers can monitor and control all activities necessary during farming and be advised $24\times 7$ through from pre-farming to post-farming his low featured mobile device.
Filterless multi-level delta-sigma class-D amplifier for portable applications

A 2W filterless class-D amplifier using multi-level delta-sigma modulation is presented. LC filters consume large PCB space and add significant system cost, so filterless class-D solutions are preferred for portable applications. In this design, a multi-level, delta-sigma modulator is implemented to achieve high efficiency and high performance without the use of a filter. Although the modulator internally generates an 8MHz clock, the average switching frequency of output signal is dynamically reduced to 300kHz to minimize switching loss. Analog feedback from the output stages along with common-mode control yields 0.003% total harmonic distortion, and 103dB dynamic range.
Automated library recommendation

Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association rule mining and collaborative filtering. The association rule mining component recommends libraries based on a set of library usage patterns. The collaborative filtering component recommends libraries based on those that are used by other similar projects. We investigate the effectiveness of our hybrid approach on 500 software projects that use many third-party libraries. Our experiments show that our approach can recommend libraries with recall rate@5 of 0.852 and recall rate@10 of 0.894.
Efficient Exploration in Monte Carlo Tree Search using Human Action Abstractions

Monte Carlo Tree Search (MCTS) is a family of methods for planning in large domains. It focuses on finding a good action for a particular state, making its complexity independent of the size of the state space. However such methods are exponential with respect to the branching factor. Effective application of MCTS requires good heuristics to arbitrate action selection during learning. In this paper we present a policy-guided approach that utilizes action abstractions, derived from human input, with MCTS to facilitate efficient exploration. We draw from existing work in hierarchical reinforcement learning, interactive machine learning and show how multi-step actions, represented as stochastic policies, can serve as good action selection heuristics. We demonstrate the efficacy of our approach in the PacMan domain and highlight its advantages over traditional MCTS.
Pedestrian detection by means of far-infrared stereo vision

This article presents a stereo system for the detection of pedestrians using far-infrared cameras. Since pedestrian detection in far-infrared images can be difficult in some environmental conditions, the system exploits three different detection approaches: warm area detection, edge-based detection, and disparity computation. A final validation process is performed using head morphological and thermal characteristics. Currently, neither temporal correlation, nor motion cues are used in this processing. The developed system has been implemented on an experimental vehicle equipped with two infrared cameras and preliminarily tested in different situations.
Theoretical and practical analysis of a current sensing principle that exploits the resistance of the copper trace

The output current of contemporary AC-DC front-end converters is steadily increasing due to higher power density requirements. The use of shunt resistors to sense the output current is becoming unacceptable due to high power losses and new techniques for current sensing need to be investigated. In this paper, we investigate the possible use of the voltage drop across the trace resistance to sense the output current. This approach promises very low cost since no dedicated shunt resistor is required, no additional power losses occur and no extra space on the printed-circuit-board (PCB) is necessary. To overcome the problems associated with the temperature drift of the copper, and variability in the trace resistance of the copper track, a digital controller can be used to calibrate the trace resistance and implement a temperature drift compensation. This form of microcontroller is readily available on todaypsilas AC-DC front-end converters. However, theoretical and practical investigations revealed that the parasitic inductance and skin effect may limit the bandwidth of this measurement principle down to several hundred Hz. To overcome this limitation, we proposed a compensation technique that has the potential to increase the bandwidth beyond ten kilohertz. Experiments with output currents of up to 240 A demonstrated that the measurement uncertainty for DC currents is less than plusmn1 A for temperatures between 25 to 60 degrees, and that the effective bandwidth can be enhanced using a compensation technique.
A Fully Automatic Crossword Generator

This paper presents a software system that is able to generate crosswords with no human intervention including definition generation and crossword compilation. In particular, the proposed system crawls relevant sources of the Web, extracts definitions from the downloaded pages using state-of-the-art natural language processing (NLP) techniques and, finally, attempts at compiling a crossword schema with the extracted definitions using a constrain satisfaction programming (CSP) solver. The crossword generator has relevant applications in entertainment, educational and rehabilitation contexts.
A Multichannel Approach to Fingerprint Classification

Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4,000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.
Detection of Red Tomato on Plants using Image Processing Techniques

Tomatoes are the best-known grown fruit in greenhouses that have been recently attempted to be picked up automatically. Tomato is a plant which its fruit does not ripe simultaneously, therefore it is necessary to develop an algorithm to distinguish red tomatoes. In the current study, a new segmentation algorithm based on region growing was proposed for guiding a robot to pick up red tomatoes. For this purpose, several colour images of tomato plants were acquired in a greenhouse. The colour images of tomato were captured under natural light, without any artificial lighting equipment. To recognize red tomatoes form non-red ones, at first background of images were removed. For removing the background, subtraction of red and green components (R-G) was applied. Usually tomatoes touch together, so separating touching tomatoes was next step. In this step, the watershed algorithm was used that was followed by improving process. Afterwards, red tomato was detected by the region growing approach. Results obtained from testing the developed algorithm showed an encouraging accuracy (82.38%) to develop an expert system for online recognition of red
Gigantomastia--definition and association with hypercalcaemia.

We read with some concern the letter from Rees et al. The authors report the disastrous complication of complete nasal soft tissue envelope necrosis. This followed an attempt to expand an area of ‘extreme shortage of skin following multiple previous operations for a cleft lip and nose’ using an Osmed osmotic expander. We find it rather surprising that Rees et al. can point their finger at the device for such a catastrophic complication when a basic principle of tissue expansion avoidance of donor sites compromised by previous scars or trauma was clearly overlooked. In addition, when using a new device with unfamiliar characteristics, frequent review of the patient would seem sensible, particularly if the skin appears ischaemic ‘immediately following insertion’. We were therefore, rather confused by the comment that ‘once the tissue began to necrose there was no stopping the process without removing the expander’. Self-expanders herald a new era of more precise, targeted tissue expansion, andareespeciallyuseful inpaediatric facial reconstruction. The final size of the device can readily be controlled by customising its initial proportions and the speed of expansion can be controlled by modifying the composition of polymer. Clinical experience has been positive, with no tissue loss associated with any of these devices. We have used self expanders with uniformly good results in eight rhinoplasty patients, without ever seeing skin compromise. In summary, when used correctly, the technology is safe and represents a major leap forward in nasal reconstruction.
Imbalanced Protein Data Classification Using Ensemble FTM-SVM

Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems. The protein in one class are seen as positive examples while those outside the class are seen as negative examples. However, the class imbalance problem will arise in this case because the number of protein in one class is usually much smaller than that of the protein outside the class. To handle the challenge, we propose a novel framework to classify the protein. We firstly use free scores (FS) to perform feature extraction for protein; then, the inverse random under sampling (IRUS) is used to create a large number of distinct training sets; next, we use a new ensemble approach to combine these distinct training sets with a new fuzzy total margin support vector machine (FTM-SVM) that we have constructed. we call the novel ensemble classifier as ensemble fuzzy total margin support vector machine (EnFTM-SVM). We then give a full description of our method, including the details of its derivation. Finally, experimental results on fourteen benchmark protein data sets indicate that the proposed method outperforms many state-of-the-art protein classifying methods.