nils-herrmann/clean_scirepeval huggingface dataset:
Music emotion classification and context-based music recommendation

Context-based music recommendation is one of rapidly emerging applications in the advent of ubiquitous era and requires multidisciplinary efforts including low level feature extraction and music classification, human emotion description and prediction, ontology-based representation and recommendation, and the establishment of connections among them. In this paper, we contributed in three distinctive ways to take into account the idea of context awareness in the music recommendation field. Firstly, we propose a novel emotion state transition model (ESTM) to model human emotional states and their transitions by music. ESTM acts like a bridge between user situation information along with his/her emotion and low-level music features. With ESTM, we can recommend the most appropriate music to the user for transiting to the desired emotional state. Secondly, we present context-based music recommendation (COMUS) ontology for modeling user’s musical preferences and context, and for supporting reasoning about the user’s desired emotion and preferences. The COMUS is music-dedicated ontology in OWL constructed by incorporating domain-specific classes for music recommendation into the Music Ontology, which includes situation, mood, and musical features. Thirdly, for mapping low-level features to ESTM, we collected various high-dimensional music feature data and applied nonnegative matrix factorization (NMF) for their dimension reduction. We also used support vector machine (SVM) as emotional state transition classifier. We constructed a prototype music recommendation system based on these features and carried out various experiments to measure its performance. We report some of the experimental results.
Osmotic Heat Engine Using Thermally Responsive Ionic Liquids.

The osmotic heat engine (OHE) is a promising technology for converting low grade heat to electricity. Most of the existing studies have focused on thermolytic salt systems. Herein, for the first time, we proposed to use thermally responsive ionic liquids (TRIL) that have either an upper critical solution temperature (UCST) or lower critical solution temperature (LCST) type of phase behavior as novel thermolytic osmotic agents. Closed-loop TRIL-OHEs were designed based on these unique phase behaviors to convert low grade heat to work or electricity. Experimental studies using two UCST-type TRILs, protonated betaine bis(trifluoromethyl sulfonyl)imide ([Hbet][Tf2N]) and choline bis(trifluoromethylsulfonyl)imide ([choline][Tf2N]) showed that (1) the specific energy of the TRIL-OHE system could reach as high as 4.0 times that of the seawater and river water system, (2) the power density measured from a commercial FO membrane reached up to 2.3 W/m2, and (3) the overall energy efficiency reached up to 2.6% or 18% of the Carnot efficiency at no heat recovery and up to 10.5% or 71% of the Carnet efficiency at 70% heat recovery. All of these results clearly demonstrated the great potential of using TRILs as novel osmotic agents to design high efficient OHEs for recovery of low grade thermal energy to work or electricity.
A Transceiver Module for FMCW Radar Sensors Using 94-GHz Dot-Type Schottky Diode Mixer

In this study, we fabricated a 94-GHz transceiver module for a millimeter-wave (MMW) frequency modulation continuous wave (FMCW) radar sensor. The transceiver modules consist of a waveguide voltage-controlled oscillator (VCO) and Rx module using a single balanced mixer. We designed a mixer with a conversion loss of 6.4 dB, without using an amplifier. Also, the waveguide VCO consisted of an InP Gunn diode, a varactor diode, two bias posts with LPF, and a Magic Tee for the MMW radar transceiver. The fabricated VCO has a tuning range of 1280 MHz by a varactor bias of 0~20 V, 1.69% linearity range of 680 MHz, and current consumption of 154 to 157 mA. The completed module has a good conversion loss of 10.6 dB with an LO power of 11.4 dBm at 94 GHz. With this RF and LO input power, the conversion loss was maintained between 10.2-11.5 dB in the RF frequency range of 93.67-94.95 GHz.
GA3C: GPU-based A3C for Deep Reinforcement Learning

We introduce and analyze the computational aspects of a hybrid CPU/GPU implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. Our analysis concentrates on the critical aspects to leverage the GPU’s computational power, including the introduction of a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. We also show the potential for the use of larger DNN models on a GPU. Our TensorFlow implementation achieves a significant speed up compared to our CPU-only implementation, and it will be made publicly available to other researchers.
Toxicity detection in multiplayer online games

Social interactions in multiplayer online games are an essential feature for a growing number of players world-wide. However, this interaction between the players might lead to the emergence of undesired and unintended behavior, particularly if the game is designed to be highly competitive. Communication channels might be abused to harass and verbally assault other players, which negates the very purpose of entertainment games by creating a toxic player-community. By using a novel natural language processing framework, we detect profanity in chat-logs of a popular Multiplayer Online Battle Arena (MOBA) game and develop a method to classify toxic remarks. We show how toxicity is non-trivially linked to game success.
Providing privacy, safety, and security in IoT-based transactive energy systems using distributed ledgers

Power grids are undergoing major changes due to rapid growth in renewable energy resources and improvements in battery technology. While these changes enhance sustainability and efficiency, they also create significant management challenges as the complexity of power systems increases. To tackle these challenges, decentralized Internet-of-Things (IoT) solutions are emerging, which arrange local communities into transactive microgrids. Within a transactive microgrid, "prosumers" (i.e., consumers with energy generation and storage capabilities) can trade energy with each other, thereby smoothing the load on the main grid using local supply. It is hard, however, to provide security, safety, and privacy in a decentralized and transactive energy system. On the one hand, prosumers' personal information must be protected from their trade partners and the system operator. On the other hand, the system must be protected from careless or malicious trading, which could destabilize the entire grid. This paper describes Privacy-preserving Energy Transactions (PETra), which is a secure and safe solution for transactive microgrids that enables consumers to trade energy without sacrificing their privacy. PETra builds on distributed ledgers, such as blockchains, and provides anonymity for communication, bidding, and trading.
Lightweight Secure RPL: A Need in IoT

The Internet of Things (IoT) is a term which indicates the accessibility of everyday objects or things over the internet. These objects can be sensors, actuators etc. IoT supports various applications like Building & home automation, Smart cities, Smart energy, Wearables, Health care etc. but these applications are suffering from the security issues as these are accessible over the internet. In IoT, 6LowPAN is the layer which takes care of routing and uses the RPL (Routing for Low Power and Lossy Network) protocol. Current specification of RPL has many vulnerabilities which in turn causes attacks. So there is a need for improvement of this protocol. In this paper we have discussed gaps in the implementation of RPL protocol which causes security attacks and also proposed the solutions to overcome these issues.
Intelligent phishing url detection using association rule mining

Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining—apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.
Degradation of methyl orange using short-wavelength UV irradiation with oxygen microbubbles.

A novel wastewater treatment technique using 8 W low-pressure mercury lamps in the presence of uniform-sized microbubbles (diameter = 5.79 microm) was investigated for the decomposition of methyl orange as a model compound in aqueous solution. Photodegradation experiments were conducted with a BLB black light blue lamp (365 nm), a UV-C germicidal lamp (254 nm) and an ozone lamp (185 nm+254 nm) both with and without oxygen microbubbles. The results show that the oxygen microbubbles accelerated the decolorization rate of methyl orange under 185+254 nm irradiation. In contrast, the microbubbles under 365 and 254 nm irradiation were unaffected on the decolorization of methyl orange. It was found that the pseudo-zero order decolorization reaction constant in microbubble system is 2.1 times higher than that in conventional large bubble system. Total organic carbon (TOC) reduction rate of methyl orange was greatly enhanced by oxygen microbubble under 185+254 nm irradiation, however, TOC reduction rate by nitrogen microbubble was much slower than that with 185+254 nm irradiation only. Possible reaction mechanisms for the decolorization and mineralization of methyl orange both with oxygen and nitrogen mirobubbles were proposed in this study.
WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications

A vocoder-based speech synthesis system, named WORLD, was developed in an effort to improve the sound quality of realtime applications using speech. Speech analysis, manipulation, and synthesis on the basis of vocoders are used in various kinds of speech research. Although several high-quality speech synthesis systems have been developed, real-time processing has been difficult with them because of their high computational costs. This new speech synthesis system has not only sound quality but also quick processing. It consists of three analysis algorithms and one synthesis algorithm proposed in our previous research. The effectiveness of the system was evaluated by comparing its output with against natural speech including consonants. Its processing speed was also compared with those of conventional systems. The results showed that WORLD was superior to the other systems in terms of both sound quality and processing speed. In particular, it was over ten times faster than the conventional systems, and the real time factor (RTF) indicated that it was fast enough for real-time processing. key words: speech analysis, speech synthesis, vocoder, sound quality, realtime processing
Resource and Deadline-Aware Job Scheduling in Dynamic Hadoop Clusters

As Hadoop is becoming increasingly popular in large-scale data analysis, there is a growing need for providing predictable services to users who have strict requirements on job completion times. While earliest deadline first scheduling (EDF) like algorithms are popular in guaranteeing job deadlines in real-time systems, they are not effective in a dynamic Hadoop environment, i.e., a Hadoop cluster with dynamically available resources. As there is a growing number of Hadoop clusters deployed on hybrid systems, e.g., infrastructure powered by mix of traditional and renewable energy, and cloud platforms hosting heterogeneous workloads, variable resource availability becomes common when running Hadoop jobs. In this paper, we propose, RDS, a Resource and Deadline-aware Hadoop job Scheduler that takes future resource availability into consideration when minimizing job deadline misses. We formulate the job scheduling problem as an online optimization problem and solve it using an efficient receding horizon control algorithm. To aid the control, we design a self-learning model to estimate job completion times and use a simple but effective model to predict future resource availability. We have implemented RDS in the open source Hadoop implementation and performed evaluations with various benchmark workloads. Experimental results show that RDS substantially reduces the penalty of deadline misses by at least 36% and 10% compared with Fair Scheduler and EDF scheduler, respectively.
Co-regularized Multi-view Spectral Clustering

In many clustering problems, we have access to multiple view s of the data each of which could be individually used for clustering. Exploit ing information from multiple views, one can hope to find a clustering that is more a ccurate than the ones obtained using the individual views. Often these diffe rent views admit same underlying clustering of the data, so we can approach this pr oblem by looking for clusterings that are consistent across the views, i.e., cor responding data points in each view should have same cluster membership. We propose a s pectral clustering framework that achieves this goal by co-regularizing th e clustering hypotheses, and propose two co-regularization schemes to accompli sh this. Experimental comparisons with a number of baselines on two synthetic and t hree real-world datasets establish the efficacy of our proposed approaches.
Towards a New Structural Model of the Sense of Humor: Preliminary Findings

In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factormodel of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.
The importance of usability in product choice: a mobile phone case study.

Usability has become established as an important aspect of product design. This paper describes an investigation that was carried out to identify the importance of usability in product choice related to other product attributes. Interviews were initially carried out to identify possible attributes that contribute to product choice. Experiments were then undertaken using the methods of active information search, structured preference elicitation, ranking and interviews in order to find out what attributes were important to people when choosing an example product - mobile phones. It was found that usability is indeed important in product choice but perhaps not as much as users themselves believe. Other attributes that were found to be more important were features, aesthetics and cost. The process of product choice was found to be complex and it may be the case that people have come to expect usability in their products. Further research is needed to understand more fully the changing role of usability in product choice and to further improve the quality of the user-product relationship.
Tip Refinement Grafts: The Designer Tip.

In cosmetic rhinoplasty, the patient's satisfaction is most often determined by the quality of the tip surgery, but perfecting a technique for consistently attractive tips can be challenging. As a result, rhinoplasty surgery is now entering a new era of "designer tip" operations, wherein surgeons can employ a combination of open suture tip techniques and tip refinement grafts to achieve consistent results. The grafts are made from excised lateral crural cartilage and, depending upon the specific aesthetic goals, the shape can include the following:domal, shield, diamond, folded, or combination. It is possible to alter dome-defining points, tip point, projection,definition, volume, and size and shape. A study of 100 consecutive female rhinoplasties indicated that tip sutures alone were used in 36% of cases, while a tip refinement graft was added to a sutured tip in 59% of cases.
An Approach to Computing Ethics

It might seem impossible to "compute" ideas that humans feel most passionately about and have such difficulty codifying: their ethical beliefs. We've been attempting to make ethics computable for three reasons. First, to avert possible harmful behavior from increasingly autonomous machines, we want to determine whether one can add an ethical dimension to them. Second, we want to advance the study of ethical theory by making it more precise. Finally, we want to solve a particular problem in ethical theory, to develop a decision procedure for an ethical theory that involves multiple, potentially competing, and duties. We've adopted the action-based approach to ethical theory, where the theory tells us how we should act in ethical dilemmas. This approach lends itself to machine implementation by giving the agent either a single principle or several principles to guide its actions, unlike other approaches that don't clearly specify the correct action in an ethical dilemma. The approach to computing ethics that we describe in this article is illustrated by MedEthEx, a system that uses machine learning to resolve a biomedical ethical dilemma. This, we believe, lends support for our approach to computing ethics
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat Email addresses: chaene@eecs.berkeley.edu (Christian Häne), lionel_heng@dso.org.sg (Lionel Heng), gimhee.lee@nus.edu.sg (Gim Hee Lee), fraundorfer@icg.tugraz.at (Friedrich Fraundorfer), paul.furgale@mavt.ethz.ch (Paul Furgale), sattlert@inf.ethz.ch (Torsten Sattler), marc.pollefeys@inf.ethz.ch ( 2017 ar X iv :1 70 8. 09 83 9v 1 [ cs .C V ] 3 1 A ug 2 01 7 each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction.
Dimensions of the Dysexecutive Questionnaire (DEX) examined using Rasch analysis.

Executive dysfunction is very broad term used to capture a range of interacting high-level cognitive, emotional and behavioural difficulties, commonly seen after acquired brain injury (ABI). Many argue that the concept of executive functioning should be subdivided into several separate dimensions. In this study we explore the dimensional structure of a tool designed to assess level of executive functioning, the Dysexecutive (DEX) Questionnaire (Burgess, Alderman, Wilson, Evans, & Emslie, 1996), in order to inform theoretical conceptualisations of executive functioning and improve measurement precision in rehabilitation centres. Rasch analysis was undertaken on the responses of 363 people with ABI to the DEX. Overall, the questionnaire did not perform as a unidimensional, interval-level scale of executive functioning, suggesting that it measures more than one psychological construct. Most subscales previously proposed using factor analysis, including those suggested in the DEX manual, did not perform as unidimensional interval-level scales either. Several new subscales in keeping with theoretical conceptualisations of the different dimensions of executive functioning are proposed, alongside suggestions for revision of the wording and scoring of some of the items in the DEX. These results provide a platform for future evaluation of executive function rehabilitation programmes.
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.