nils-herrmann/clean_scirepeval huggingface dataset:
Learning 6-DOF Grasping Interaction via Deep Geometry-Aware 3D Representations

This paper focuses on the problem of learning 6- DOF grasping with a parallel jaw gripper in simulation. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. We introduce a deep geometry-aware grasping network (DGGN) that decomposes the learning into two steps. First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10% relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances.
GPS Spoofing Detection via Dual-Receiver Correlation of Military Signals

Cross-correlation of unknown encrypted signals between two Global Navigation Satellite System (GNSS) receivers is used for spoofing detection of publicly-known signals. This detection technique is one of the strongest known defenses against sophisticated spoofing attacks if the defended receiver has only one antenna. The attack strategy of concern overlays false GNSS radio-navigation signals on top of the true signals. The false signals increase in power, lift the receiver tracking loops off of the true signals, and drag the loops and the navigation solution to erroneous but consistent results. Hypothesis testing theory is used to develop a codeless cross-correlation detection method for use in inexpensive, narrowband civilian GNSS receivers. The detection method is instantiated by using the encrypted military Global Positioning System (GPS) P(Y) code on the L1 frequency in order to defend the publicly-known civilian GPS C/A code. Successful detection of spoofing attacks is demonstrated by off-line processing of recorded RF data from narrowband 2.5 MHz RF front-ends, which attenuate the wideband P(Y) code by 5.5 dB. The new technique can detect attacks using correlation intervals of 1.2 s or less.
Comparison of eSports and Traditional Sports Consumption Motives

With recognition of the need for studying eSports in this interactive digital communication era, this study explored 14 motivational factors affecting the time spent on eSports gaming. Using a sample of 515 college students and athletic event attendees, we further compared eSports game patterns to their non-eSport or traditional sport involvements (game participation, game attendance, sports viewership, sports readership, sports listenership, Internet usage specific to sports, and purchase of team merchandise). Multiple regression results indicated that competition and skill had a statistically significant impact on the time spent on eSports games while peer pressure had marginal significance. Related to the overall findings, developing tailored messages that drives consumption behaviors of target audiences to specific eSports games will provide a better chance for marketers to fulfill their strategic goals of increased purchasing and larger market shares. Understanding that the interest in competition and skill are critical to eSports gamers may influence marketers to focus on creating games and opportunities for gamers to compete against each other and give tangible rewards to the winner. The use of peer pressure may be another motivational factor for playing. Consequently, those marketing dollars could be spent more on the interactive nature of game design. The subsequent analysis on cross-validation check suggests that the results of the regression analysis could be generalized.
A Novel, Planar, and Compact Crossover Design for Dual-Band Applications

This paper presents, for the first time, the novel design of a dual-band crossover. The proposed circuit features planar implementation with moderate line impedance, compact layout, and widely separated frequency bands. Moreover, simple and explicit closed-form expressions are available for the exact evaluation of the line impedances. For verification, both simulated and measured results of a microstrip crossover operating at 800 MHz and 1.2 GHz are given.
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.
Detection of data injection attack in industrial control system using long short term memory recurrent neural network

In 2010, the outbreak of Stuxnet sounded a warning in the field of industrial control.security. As the major attack form of Stuxnet, data injection attack is characterized by high concealment and great destructiveness. This paper proposes a new method to detect data injection attack in Industrial Control Systems (ICS), in which Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is a temporal sequences predictor. We then use the Euclidean detector to identify attacks in a model of a chemical plant. With simulation and evaluation in Tennessee Eastman (TE) process, we show that this method is able to detect various types of data injection attacks.
DifNet: Semantic Segmentation by Diffusion Networks

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once, since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. Our DifNet is trained end-to-end without complex loss functions.
HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT

Recent developments in memory-augmented neural networks allowed sequential problems requiring long-term memory to be solved, which were intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to an external memory component can be encoded geometrically through a novel HyperNEAT-based Neural Turing Machine (HyperNTM). The indirect HyperNEAT encoding allows for training on small memory vectors in a bit vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy nine bit vectors can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, the HyperNTM approach could now allow memory-augmented neural networks to scale to problems requiring large memory vectors and sequence lengths.
Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning

The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the gaming industry. However, a secondary consideration is whether the autonomous bots behave like human players; this is especially important for simulation/training applications which aim to instruct participants in real-world tasks. Bots often compensate for a lack of combat acumen with advantages such as accurate targeting, predefined navigational networks, and perfect world knowledge, which makes them challenging but often predictable opponents. In this paper, we examine the problem of teaching a bot to play like a human in first-person shooter game combat scenarios. Our bot learns attack, exploration and targeting policies from data collected from expert human player demonstrations in Unreal Tournament. We hypothesize that one key difference between human players and autonomous bots lies in the relative valuation of game states. To capture the internal model used by expert human players to evaluate the benefits of different actions, we use inverse reinforcement learning to learn rewards for different game states. We report the results of a human subjects’ study evaluating the performance of bot policies learned from human demonstration against a set of standard bot policies. Our study reveals that human players found our bots to be significantly more human-like than the standard bots during play. Our technique represents a promising stepping-stone toward addressing challenges such as the Bot Turing Test (the CIG Bot 2K Competition).
Solving traveling salesman problems via artificial intelligent search techniques

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. It is also considered as the class of the NPcomplete combinatorial optimization problems. By literatures, many algorithms and approaches have been launched to solve such the TSP. However, no current algorithms that can provide the exactly optimal solution of the TSP problem are available. This paper proposes the application of AI search techniques to solve the TSP problems. Three AI search methods, i.e. genetic algorithms (GA), tabu search (TS), and adaptive tabu search (ATS), are conducted. They are tested against ten benchmark real-world TSP problems. As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems.  Traveling Salesman Problem, Genetic Algorithm, Tabu Search, Adaptive Tabu Search
Parallel Training of Neural Networks for Speech Recognition

In this paper we describe parallel implementation of ANN training procedure based on block mode back-propagation learning algorithm. Two different approaches to parallelization were implemented. The first is data parallelization using POSIX threads, it is suitable for multi-core computers. The second is node parallelization using high performance SIMD architecture of GPU with CUDA, suitable for CUDA enabled computers. We compare the speed-up of both approaches by learning typically-sized network on the real-world phoneme-state classification task, showing nearly 10 times reduction when using CUDA version, while the 8-core server with multi-thread version gives only 4 times reduction. In both cases we compared to an already BLAS optimized implementation. The training tool will be released as Open-Source software under project name TNet.
A survey of unknown object grasping and our fast grasping algorithm-C shape grasping

Grasping of unknown objects with neither appearance data nor object models given in advance is very important for robots that work in an unfamiliar environment. In recent years, extensive research has been conducted in the domain of unknown object grasping and many successful grasping algorithms for unknown objects are created. However, So far there is not a very general fast grasping algorithm suits various kinds of unknown objects. Therefore, choice among different grasping algorithms becomes necessary for users. In order to make it more convenient for users to quickly understand and choose a suitable grasping algorithm, a survey about the latest research results of unknown object grasping is made in this paper. We compared different grasping algorithms with each other and obtained a table to clearly show the result of comparison. The comparison could give researchers meaningful information in order to quickly pick a grasping approach with their requirements. Meanwhile, we briefly showed our latest fast grasping algorithm which employs only a partial point cloud of the target object as input, and the grasping algorithm can quickly work out a suitable grasp for most objects within 2 seconds on a common personal computer. Simulations are used to examine the performance of our algorithm and successful results are obtained.
Search Ranges Efficiently and Compatibly as Keywords over Encrypted Data

With recent studies in Searchable Symmetric Encryption (SSE), a client can efficiently perform keyword queries over its outsourced data on a remote but untrusted server (e.g., a public cloud), and correctly retrieve associated files without revealing the confidentiality of his/her data. Besides keyword search, many recent schemes also studied range queries on encrypted data, where range search is also one of the most extensively used queries in databases and information retrieval. However, most of these previous works supporting range search are neither efficient nor compatible with existing keyword SSE schemes. In this paper, we propose two range SSE schemes to enable range queries on encrypted data. Both of our schemes are not only efficient, but also highly compatible with existing keyword SSE schemes. Specifically, the search time of our first scheme is extremely efficient when the values in a range query are sparsely presenting in a dataset; while our second design can achieve an optimal token size and significantly save token generation costs. Moreover, we rigorously define and analyze the security of our schemes, and also conduct extensive experiments with a real dataset to demonstrate the performance of our schemes.
Running on the bare metal with GeekOS

Undergraduate operating systems courses are generally taught using one of two approaches: abstract or concrete. In the abstract approach, students learn the concepts underlying operating systems theory, and perhaps apply them using user-level threads in a host operating system. In the concrete approach, students apply concepts by working on a real operating system kernel. In the purest manifestation of the concrete approach, students implement operating system projects that run on real hardware.GeekOS is an instructional operating system kernel which runs on real hardware. It provides the minimum functionality needed to schedule threads and control essential devices on an x86 PC. On this foundation, we have developed projects in which students build processes, semaphores, a multilevel feedback scheduler, paged virtual memory, a filesystem, and inter-process communication. We use the Bochs emulator for ease of development and debugging. While this approach (tiny kernel run on an emulator) is not new, we believe GeekOS goes further towards the goal of combining realism and simplicity than previous systems have.
Estimation of Blood Pressure and Pulse Transit Time Using Your Smartphone

It is widely recognized today that there is an alarming rise of lifestyle-induced chronic diseases (e.g., type II diabetes) in our society. Therefore, a strong need exists for cost-effective and non-invasive devices that can measure blood pressure (BP) to monitor, diagnose and follow-up patients at risk, but also healthy population in general. One promising method for arterial BP estimation is to measure a surrogate marker of it, such as, Pulse Transit Time (PTT) and derive pressure values from it. However, current methods for measuring PTT require complex sensing and analysis circuitry and the related medical devices are expensive and inconvenient for the user to wear. In this paper, we present a new smartphone-based method to estimate PTT reliably and subsequently BP from the baseline sensors on smartphones. This new approach involves determining PTT by simultaneously measuring the time the blood leaves the heart, by recording the heart sound using the standard microphone of the phone and the time it reaches the finger, by measuring the pulse wave using the phone's camera. Moreover, we also describe algorithms that can be executed directly on current smartphones to obtain clean and robust heart sound signals and to extract the pulse wave characteristics using smartphones. We also present methods to ensure a synchronous capture of the waveforms, which is essential to obtain reliable PTT values with inexpensive sensors. Our experiments show that the computational overhead of the proposed two-phase processing method is minimum, with the ability to reliably measure the PTT values in a fully accurate (beat-to-beat) fashion using directly state-of-the-art smartphones as medical devices.
WebQual: An Exploration of Web-Site Quality

The issue of web-site quality is tackled from the perspective of the ‘voice of the customer’. Quality function deployment (QFD) is adopted as a framework for identifying web-site qualities demanded by users, which are gathered through a quality workshop. From the workshop an instrument for assessing web-site quality is developed (WebQual) and tested in the domain of UK business schools. The results of the WebQual survey are presented and analyzed, leading to the generation of a WebQual Index of web-site quality. Work under way to extend and refine the WebQual instrument includes electronic commerce evaluation, where web-site service quality is proposed as a key issue.
PUF-FSM: A Controlled Strong PUF

Existing strong controlled physical unclonable function (PUF) designs are built to resist modeling attacks and they deal with noisy PUF responses by exploiting error correction logic. These designs are burdened by the costs of the error correction logic and information shown to leak through the associated helper data for assisting error corrections; leaving the design vulnerable to fault attacks or reliability-based attacks. We present a hybrid PUF–finite state machine (PUF-FSM) construction to realize a controlled strong PUF. The PUF-FSM design removes the need for error correction logic and related computation, storage of the helper data and loading it on-chip by only employing error-free responses judiciously determined on demand in the absence of the underlying PUF—an Arbiter PUF—with a large challenge response pair space. The PUF-FSM demonstrates improved security, especially to reliability-based attacks and is able to support a range of applications from authentication to more advanced cryptographic applications built upon shared keys. We experimentally validate the practicability of the PUF-FSM.
Evaluation Techniques for Storage Hierarchies

This paper introduces an efficient technique called “stack processing” that can be used in the cost-performance evaluation of a large class of storage hierarchies. The technique depends on a classification of page replacement algorithms as “stack algorithms” for which various properties are derived. These properties may be of use in the general areas of program modeling and system analysis, as well as in the evaluation of storage hierarchies. For a better understanding of storage hierarchies, we briefly review some basic concepts of their design.
Platelet-rich fibrin (PRF): a second-generation platelet concentrate. Part I: technological concepts and evolution.

Platelet-rich fibrin (PRF) belongs to a new generation of platelet concentrates geared to simplified preparation without biochemical blood handling. In this initial article, we describe the conceptual and technical evolution from fibrin glues to platelet concentrates. This retrospective analysis is necessary for the understanding of fibrin technologies and the evaluation of the biochemical properties of 3 generations of surgical additives, respectively fibrin adhesives, concentrated platelet-rich plasma (cPRP) and PRF. Indeed, the 3-dimensional fibrin architecture is deeply dependent on artificial clinical polymerization processes, such as massive bovine thrombin addition. Currently, the slow polymerization during PRF preparation seems to generate a fibrin network very similar to the natural one. Such a network leads to a more efficient cell migration and proliferation and thus cicatrization.
A hybrid cryptography algorithm for cloud computing security

In the present scenario, the Cloud Computing is very famous and flexible technology. It facilitates customers with effortlessness, quickness, competence etc in their work area through the services. Cloud provides huge data centre to handle the large amount of information. The Cloud Computing benefits the organizations to handle their large volume of information. The main issue in cloud computing is data security, because number of customers are sharing same cloud. This study aimed at designing a new security method by using a hybrid cryptosystem, for data security in the cloud. The need for the current investigation is to protect data from unauthorized access or hackers in cloud at the time of data transmission by encrypting the user data. Cloud computing constitutes several security issues including data access control, identity management, auditing, integrity control and risk management therefore, this hybrid cryptosystem is designed and comprises of both symmetric and asymmetric cryptography algorithm in which Blowfish symmetric algorithm deals with data confidentiality whereas, RSA asymmetric algorithm deals with an authentication. This method also includes the Secure Hash Algorithm −2 for data integrity. The present study concluded that the proposed method provides high security on data transmission over the internet and proper network access on demand to a shared tank of constructive computing resources, mainly net, server, and storage application.
Artificial intelligent firewall

Firewalls are now an integral part of network security. An intelligent firewall that prevents unauthorized access to a system has been developed. Artificial intelligence applications are uniquely suited for the ever-changing, ever-evolving world of network security. Typical firewalls are only as good as the information provided by the Network Administrator. A new type of attack creates vulnerabilities, which a static firewall does not have the ability to avoid without human direction. An AI-managed firewall service, however, can protect a computer network from known and future threats. We report in this paper on research in progress concerning the integration of different security techniques. A main purpose of the project is to integrate a smart detection engine into a firewall. The smart detection engine will aim at not only detecting anomalous network traffic as in classical IDSs, but also detecting unusual structures data packets that suggest the presence of virus data. We will report in this paper on the concept of an intelligent firewall that contains a smart detection engine for potentially malicious data packets.