nils-herrmann/clean_scirepeval huggingface dataset:
A Comparative Study on Neural Network Based Soccer Result Prediction

This study mainly remarks the efficiency of black-box modeling capacity of neural networks in the case of forecasting soccer match results, and opens up several debates on the nature of prediction and selection of input parameters. The selection of input parameters is a serious problem in soccer match prediction systems based on neural networks or statistical methods. Several input vector suggestions are implemented in literature which is mostly based on direct data from weekly charts. Here in this paper, two different input vector parameters have been tested via learning vector quantization networks in order to emphasize the importance of input parameter selection. The input vector parameters introduced in this study are plain and also meaningful when compared to other studies. The results of different approaches presented in this study are compared to each other, and also compared with the results of other neural network approaches and statistical methods in order to give an idea about the successful prediction performance. The paper is concluded with discussions about the nature of soccer match forecasting concept that may draw the interests of researchers willing to work in this area.
Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition

This paper presents the first attempt at fusing data from inertial and vision depth sensors within the framework of a hidden Markov model for the application of hand gesture recognition. The data fusion approach introduced in this paper is general purpose in the sense that it can be used for recognition of various body movements. It is shown that the fusion of data from the vision depth and inertial sensors act in a complementary manner leading to a more robust recognition outcome compared with the situations when each sensor is used individually on its own. The obtained recognition rates for the single hand gestures in the Microsoft MSR data set indicate that our fusion approach provides improved recognition in real-time and under realistic conditions.
Enhancing the Power Capabilities of the Stepped Septum Using an Optimized Smooth Sigmoid Profile

The typical septum polarizer that has gained popularity in the literature may be unsuitable for high-power applications, due to the sharp corners in the design. In order to address this issue, the fundamentals of the septum operation are first revisited, using a graphical visualization through full-wave analysis. A septum profiled with smooth edges is next presented, with enhanced power-handling capabilities in comparison to the stepped-septum polarizer. In this work, the sigmoid function is introduced to represent the smooth contour of the septum, and to enable diverse configurations without any discontinuities. The smooth and stepped profiles are optimized using the Particle Swarm Optimization (PSO) technique. The maximum electric-field intensity around the smooth edges is investigated using a full-wave simulator, HFSS. Our observations show that the maximum electric field is reduced by 40% in comparison to the stepped septum. In Appendix 1, the numerical approach is evaluated by comparing the exact series solution for the half-plane scattering problem with the simulated results in HFSS. In Appendix 2, a septum design with rounded edges is also studied as another possible design to reduce the maximum fields.
From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN

The effectiveness of GANs in producing images according to a specific visual domain has shown potential in unsupervised domain adaptation. Source labeled images have been modified to mimic target samples for training classifiers in the target domain, and inverse mappings from the target to the source domain have also been evaluated, without new image generation. In this paper we aim at getting the best of both worlds by introducing a symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. We define a new class consistency loss that aligns the generators in the two directions, imposing to preserve the class identity of an image passing through both domain mappings. A detailed analysis of the reconstructed images, a thorough ablation study and extensive experiments on six different settings confirm the power of our approach.
Abnormality detection using deep neural networks with robust quasi-norm autoencoding and semi-supervised learning

Abnormality detection in biomedical images is a one-class classification problem, where methods learn a statistical model to characterize the inlier class using training data solely from the inlier class. Typical methods (i) need well-curated training data and (ii) have formulations that are unable to utilize expert feedback through (a small amount of) labeled outliers. In contrast, we propose a novel deep neural network framework that (i) is robust to corruption and outliers in the training data, which are inevitable in real-world deployment, and (ii) can leverage expert feedback through high-quality labeled data. We introduce an autoencoder formulation that (i) gives robustness through a non-convex loss and a heavy-tailed distribution model on the residuals and (ii) enables semi-supervised learning with labeled outliers. Results on three large medical datasets show that our method outperforms the state of the art in abnormality-detection accuracy.
Episodic memory, semantic memory, and amnesia.

Episodic memory and semantic memory are two types of declarative memory. There have been two principal views about how this distinction might be reflected in the organization of memory functions in the brain. One view, that episodic memory and semantic memory are both dependent on the integrity of medial temporal lobe and midline diencephalic structures, predicts that amnesic patients with medial temporal lobe/diencephalic damage should be proportionately impaired in both episodic and semantic memory. An alternative view is that the capacity for semantic memory is spared, or partially spared, in amnesia relative to episodic memory ability. This article reviews two kinds of relevant data: 1) case studies where amnesia has occurred early in childhood, before much of an individual's semantic knowledge has been acquired, and 2) experimental studies with amnesic patients of fact and event learning, remembering and knowing, and remote memory. The data provide no compelling support for the view that episodic and semantic memory are affected differently in medial temporal lobe/diencephalic amnesia. However, episodic and semantic memory may be dissociable in those amnesic patients who additionally have severe frontal lobe damage.
Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud : The Smart Parking Case

Enhancing the effectiveness of city services and assisting on a more sustainable development of cities are two of the crucial drivers of the smart city concept. This paper portrays a field trial that leverages an internet of things (IoT) platform intended for bringing value to existing and future smart city infrastructures. The paper highlights how IoT creates the basis permitting integration of current vertical city services into an all-encompassing system, which opens new horizons for the progress of the effectiveness and sustainability of our cities. Additionally, the paper describes a field trial on provisioning of real time data about available parking places both indoor and outdoor. The trial has been carried out at Santander’s (Spain) downtown area. The trial takes advantage of both available open data sets as well as of a large-scale IoT infrastructure. The trial is a showcase on how added-value services can be created on top of the proposed architecture.
Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-Offs by Selective Execution

We introduce Dynamic Deep Neural Networks (DNN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of DNN neurons are executed, and the particular subset is determined by the DNN itself. By pruning unnecessary computation depending on input, DNNs provide a way to improve computational efficiency. To achieve dynamic selective execution, a DNN augments a feed-forward deep neural network (directed acyclic graph of differentiable modules) with controller modules. Each controller module is a sub-network whose output is a decision that controls whether other modules can execute. A DNN is trained end to end. Both regular and controller modules in a DNN are learnable and are jointly trained to optimize both accuracy and efficiency. Such training is achieved by integrating backpropagation with reinforcement learning. With extensive experiments of various DNN architectures on image classification tasks, we demonstrate that DNNs are general and flexible, and can effectively optimize accuracyefficiency trade-offs.
Planar antenna design at 60 GHz for high date rate point-to-point connections

Millimetre wave antennas are typically used for applications like anti-collision car radar or sensory. A new and upcoming application is the use of 60 GHz antennas for high date rate point-to-point connections to serve wireless local area networks. For high gain antennas, configurations using lenses in combination with planar structures are often applied. However, single layer planar arrays might offer a more cost-efficient solution, especially if the antenna and the RF-circuitry are realised on one and the same substrate. The design of millimetre wave antennas has to cope with the severe impacts of manufacturing tolerances and losses at these frequencies. Reproducibility can become poor in such cases. The successful design and realisation of a cost-efficient 60 GHz planar patch array (8/spl times/8 elements) with high reproducibility for point-to-point connections is presented. Important design aspects are highlighted and manufacturing tolerances and losses are analysed. Measurement results of different prototypes are presented to show the reproducibility of the antenna layout.
Real-Time Strategy Game Competitions

106 AI MAGAZINE RTS games — such as StarCraft by Blizzard Entertainment and Command and Conquer by Electronic Arts — are popular video games that can be described as real-time war simulations in which players delegate units under their command to gather resources, build structures, combat and support units, scout opponent locations, and attack. The winner of an RTS game usually is the player or team that destroys the opponents’ structures first. Unlike abstract board games like chess and go, moves in RTS games are executed simultaneously at a rate of at least eight frames per second. In addition, individual moves in RTS games can consist of issuing simultaneous orders to hundreds of units at any given time. If this wasn’t creating enough complexity already, RTS game maps are also usually large and states are only partially observable, with vision restricted to small areas around friendly units and structures. Complexity by itself, of course, is not a convincing motivation for studying RTS games and building AI systems for them. What makes them attractive research subjects is the fact that, despite the perceived complexity, humans are able to outplay machines by means of spatial and temporal reasoning, long-range adversarial planning and plan
Getting started with SUSAS: a speech under simulated and actual stress database

It is well known that the introduction of acoustic background distortion and the variability resulting from environmentally induced stress causes speech recognition algorithms to fail. In this paper, we discuss SUSAS: a speech database collected for analysis and algorithm formulation of speech recognition in noise and stress. The SUSAS database refers to Speech Under Simulated and Actual Stress, and is intended to be employed in the study of how speech production and recognition varies when speaking during stressed conditions. This paper will discuss (i) the formulation of the SUSAS database, (ii) baseline speech recognition using SUSAS data, and (iii) previous research studies which have used the SUSAS data base. The motivation for this paper is to familiarize the speech community with SUSAS, which was released April 1997 on CD-ROM through the NATO RSG.10.
A Deep Learning-based Approach for Banana Leaf Diseases Classification

Plant diseases are important factors as they result in serious reduction in quality and quantity of agriculture products. Therefore, early detection and diagnosis of these diseases are important. To this end, we propose a deep learning-based approach that automates the process of classifying banana leaves diseases. In particular, we make use of the LeNet architecture as a convolutional neural network to classify image data sets. The preliminary results demonstrate the effectiveness of the proposed approach even under challenging conditions such as illumination, complex background, different resolution, size, pose, and orientation of real scene images.
POWER8 design methodology innovations for improving productivity and reducing power

The design complexity of modern high performance processors calls for innovative design methodologies for achieving time-to-market goals. New design techniques are also needed to curtail power increases that inherently arise from ever increasing performance targets. This paper describes new design approaches employed by the POWER8 processor design team to address complexity and power consumption challenges. Improvements in productivity are attained by leveraging a new and more synthesis-centric design methodology. New optimization strategies for synthesized macros allow power reduction without sacrificing performance. These methodology innovations contributed to the industry leading performance of the POWER8 processor. Overall, POWER8 delivers a 2.5x increase in per-socket performance over its predecessor, POWER7+, while maintaining the same power dissipation.
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.
Using Hashtags as Labels for Supervised Learning of Emotions in Twitter Messages

Many college students experience depression or anxiety but do not seek help due to the social stigma associated with psychological counseling services. Automatic techniques to classify social media messages based on the emotions they express can assist in the early detection of students in need of counseling. Supervised machine learning methods yield accurate results but require training datasets of text messages that have been labelled with the classes of emotions they express. Manually labeling a large corpus of Twitter messages is labor-intensive, error prone and time-consuming. Hashtags are keywords inserted into social media messages by their authors. In this paper, we investigate using hashtags as emotion labels and evaluate them through two user studies, one with psychology experts and the other with the general crowd. The study showed that the labels created by general crowd was inconsistent and unreliable. However, the labels generated by experts matched with hashtag labels in over 87% of Twitter messages, which indicates that hashtags are indeed good emotion labels. Leveraging the concept of hashtags as emotion labels, we develop Emotex, a supervised learning approach that classifies Twitter messages into the emotion classes they express. We show that Emotex correctly classifies the emotions expressed in over 90% of text messages.
A permanent magnet motor drive without a shaft sensor

A simple control for a permanent motor drive is described which provides a wide speed range without the use of a shaft sensor. Two line-to-line voltages and two stator currents are sensed and processed in analog form to produce the stator flux linkage space vector. The angle of this vector is then used in a microcontroller to produce the appropriate stator current command signals for the hysteresis current controller of the inverter so that near unity power factor can be achieved over a wide range of torque and speed. A speed signal is derived from the rate of change of angle of the flux linkage. A drift compensation program is proposed to avoid calculation errors in the determination of angle position and speed. The control system has been implemented on a 5 kW motor using Nd-Fe-B magnets. The closed loop speed control has been shown to be effective down to a frequency of less than 1 Hz, thus providing a wide range of speed control. An open loop starting program is used to accelerate the motor up to this limit frequency with minimum speed oscillation.<<ETX>>
Technology as Moral Proxy: Autonomy and Paternalism by Design

It is often said that technological artifacts are morally neutral, that they are bereft of morality save for whatever we might say about their designers and users. Designers and users, being people, tend to be considered the proper and exclusive focus of our moral attention in the designer-technology-user trio, while technological artifacts, mere tools, cannot contribute anything morally. Technological neutrality, a common term for describing this perspective, gives rise to trite statements like, guns don't kill people, people kill people. According to technological neutrality, whatever morality is pinned on the artifact is done so in error. Instead, one must refer to the people surrounding an artifact to get an accurate read on the moral claims that can be associated with its use. Thus, technological neutrality supports a strict kind of delineation between people and things: people can be the subjects of a moral analysis; things cannot.
A new type of multi-way microwave power divider based on Bagley Polygon power divider

A new type of power divider with simple design theory is proposed based on Bagley Polygon power divider. The design theory is explained by using the equivalent circuit model. Comparing with the Bagley Polygon power divider, it takes less area on substrate board and the output ports can be neatly arranged on the board. The theoretical results are presented and discussed.
Fuzz testing in practice: Obstacles and solutions

Fuzz testing has helped security researchers and organizations discover a large number of vulnerabilities. Although it is efficient and widely used in industry, hardly any empirical studies and experience exist on the customization of fuzzers to real industrial projects. In this paper, collaborating with the engineers from Huawei, we present the practice of adapting fuzz testing to a proprietary message middleware named libmsg, which is responsible for the message transfer of the entire distributed system department. We present the main obstacles coming across in applying an efficient fuzzer to libmsg, including system configuration inconsistency, system build complexity, fuzzing driver absence. The solutions for those typical obstacles are also provided. For example, for the most difficult and expensive obstacle of writing fuzzing drivers, we present a low-cost approach by converting existing sample code snippets into fuzzing drivers. After overcoming those obstacles, we can effectively identify software bugs, and report 9 previously unknown vulnerabilities, including flaws that lead to denial of service or system crash.
Reinforcement learning to train Ms. Pac-Man using higher-order action-relative inputs

Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a specific environment. Although some very successful applications of reinforcement learning algorithms have been developed, it is still an open research question how to scale up to large dynamic environments. In this paper we will study the use of reinforcement learning on the popular arcade video game Ms. Pac-Man. In order to let Ms. Pac-Man quickly learn, we designed particular smart feature extraction algorithms that produce higher-order inputs from the game-state. These inputs are then given to a neural network that is trained using Q-learning. We constructed higher-order features which are relative to the action of Ms. Pac-Man. These relative inputs are then given to a single neural network which sequentially propagates the action-relative inputs to obtain the different Q-values of different actions. The experimental results show that this approach allows the use of only 7 input units in the neural network, while still quickly obtaining very good playing behavior. Furthermore, the experiments show that our approach enables Ms. Pac-Man to successfully transfer its learned policy to a different maze on which it was not trained before.
Segmenting Email Message Text into Zones

In the early days of email, widely-used conventions for indicating quoted reply content and email signatures made it easy to segment email messages into their functional parts. Today, the explosion of different email formats and styles, coupled with the ad hoc ways in which people vary the structure and layout of their messages, means that simple techniques for identifying quoted replies that used to yield 95% accuracy now find less than 10% of such content. In this paper, we describe Zebra, an SVM-based system for segmenting the body text of email messages into nine zone types based on graphic, orthographic and lexical cues. Zebra performs this task with an accuracy of 87.01%; when the number of zones is abstracted to two or three zone classes, this increases to 93.60% and 91.53% respectively.