nils-herrmann/clean_scirepeval huggingface dataset:
Histogram of Oriented Principal Components for Cross-View Action Recognition

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process point clouds for cross-view action recognition from unknown and unseen views. We propose the histogram of oriented principal components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of spatiotemporal keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The experimental results show that our techniques provide significant improvement over state-of-the-art methods.
A Compact Monopole Antenna for Super Wideband Applications

A planar microstrip-fed super wideband monopole antenna is initially proposed. By embedding a semielliptically fractal-complementary slot into the asymmetrical ground plane, a 10-dB bandwidth of 172% (1.44-18.8 GHz) is achieved with ratio bandwidth >;12:1. Furthermore, the proposed antenna also demonstrated a wide 14-dB bandwidth from 5.4 to 12.5 GHz, which is suitable for UWB outdoor propagation. This proposed antenna is able to cover the DVB-H in L-band (for PMP), DCS, PCS, UMTS, Bluetooth, WiMAX2500, LTE2600, and UWB bands.
Learning values across many orders of magnitude

Most learning algorithms are not invariant to the scale of the function that is being approximated. We propose to adaptively normalize the targets used in learning. This is useful in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were all clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior. Using the adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance.
Emergence of neural encoding of auditory objects while listening to competing speakers.

A visual scene is perceived in terms of visual objects. Similar ideas have been proposed for the analogous case of auditory scene analysis, although their hypothesized neural underpinnings have not yet been established. Here, we address this question by recording from subjects selectively listening to one of two competing speakers, either of different or the same sex, using magnetoencephalography. Individual neural representations are seen for the speech of the two speakers, with each being selectively phase locked to the rhythm of the corresponding speech stream and from which can be exclusively reconstructed the temporal envelope of that speech stream. The neural representation of the attended speech dominates responses (with latency near 100 ms) in posterior auditory cortex. Furthermore, when the intensity of the attended and background speakers is separately varied over an 8-dB range, the neural representation of the attended speech adapts only to the intensity of that speaker but not to the intensity of the background speaker, suggesting an object-level intensity gain control. In summary, these results indicate that concurrent auditory objects, even if spectrotemporally overlapping and not resolvable at the auditory periphery, are neurally encoded individually in auditory cortex and emerge as fundamental representational units for top-down attentional modulation and bottom-up neural adaptation.
Deep Learning for Event-Driven Stock Prediction

Neural Tensor Network for Learning Event Embeddings Event Representation E = (O1, P, O2, T), where P is the action, O1 is the actor, O2 is the object and T is the timestamp (T is mainly used for aligning stock data with news data). For example, the event “Sep 3, 2013 Microsoft agrees to buy Nokia’s mobile phone business for $7.2 billion.” is modeled as: (Actor = Microsoft, Action = buy, Object = Nokia’s mobile phone business, Time = Sep 3, 2013) Event Embedding
Individual 3D Face Generation Based on Candide-3 for Face Recognition

In this paper, an individual 3D face generation method has been proposed. This method is based on candide-3 and aims for pose normalization for face recognition system. By using ASM and AAM for facial feature point tracking and model parameters optimizing, this method decreases the computation time of 3D face generation and improves the level of the 3D model fitting with the input image. Through applying the method for pose normalization, the recognition rate can be improved greatly. Experiment results on CMU-PIE database show validity of this method.
Euthanasia and physician-assisted suicide: focus on the data.

1 MBritain and the United States began in the late 19th century. Legislation was periodically proposed only to be defeated until, in 1942, Switzerland decriminalised assistance in suicide for cases when there were no “selfish motives”. In 2002, euthanasia was legalised in the Netherlands and Belgium, then in Luxembourg in 2009, and most recently, in 2015 in Colombia and in 2016 in Canada. PAS, but not euthanasia, has been legalised in five US states. In Oregon, PAS was legalised by popular referendum in 1997. In addition, in 2009, Washington State legalised PAS by referendum and Montana by court ruling; Vermont in 2013 and California in 2015 also legalised PAS by legislation.
Object Discovery in Videos as Foreground Motion Clustering

We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We introduce a novel pixel-trajectory recurrent neural network that learns feature embeddings of foreground pixel trajectories linked in time. By clustering the pixel trajectories using the learned feature embeddings, our method establishes correspondences between foreground object masks across video frames. To demonstrate the effectiveness of our framework for object discovery, we conduct experiments on commonly used datasets for motion segmentation, where we achieve state-of-the-art performance.
Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: a meta-analytic review of the scientific literature.

Research on exposure to television and movie violence suggests that playing violent video games will increase aggressive behavior. A metaanalytic review of the video-game research literature reveals that violent video games increase aggressive behavior in children and young adults. Experimental and nonexperimental studies with males and females in laboratory and field settings support this conclusion. Analyses also reveal that exposure to violent video games increases physiological arousal and aggression-related thoughts and feelings. Playing violent video games also decreases prosocial behavior.
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed

Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.
Database forensic analysis through internal structure carving

Forensic tools assist analysts with recovery of both the data and system events, even from corrupted storage. These tools typically rely on “file carving” techniques to restore files after metadata loss by analyzing the remaining raw file content. A significant amount of sensitive data is stored and processed in relational databases thus creating the need for database forensic tools that will extend file carving solutions to the database realm. Raw database storage is partitioned into individual “pages” that cannot be read or presented to the analyst without the help of the database itself. Furthermore, by directly accessing raw database storage, we can reveal things that are normally hidden from database users. There exists a number of database-specific tools developed for emergency database recovery, though not usually for forensic analysis of a database. In this paper, we present a universal tool that seamlessly supports many different databases, rebuilding table and other data content from any remaining storage fragments on disk or in memory. We define an approach for automatically (with minimal user intervention) reverse engineering storage in new databases, for detecting volatile data changes and discovering user action artifacts. Finally, we empirically verify our tool's ability to recover both deleted and partially corrupted data directly from the internal storage of different databases.
Teleoperated robot writing using EMG signals

In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator's hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.
Free/Open Source Software Development: Recent Research Results and Methods

The focus of this chapter is to review what is known about free and open source software development (FOSSD) work practices, development processes, project and community dynamics, and other socio-technical relationships. It does not focus on specific properties or technical attributes of different FOSS systems, but it does seek to explore how FOSS is developed and evolved. The chapter provides a brief background on what FOSS is and how free software and open source software development efforts are similar and different. From there attention shifts to an extensive review of a set of empirical studies of FOSSD that articulate different levels of analysis. These characterize what has been analyzed in FOSSD studies across levels that examine why individuals participate; resources and capabilities supporting development activities; how cooperation, coordination, and control are realized in projects; alliance formation and inter-project social networking; FOSS as a multiproject software ecosystem, and FOSS as a social movement. Following this, the chapter reviews how different research methods are employed to examine different issues in FOSSD. These include reflective practice and industry polls, survey research, ethnographic studies, mining FOSS repositories, and multi-modal modeling and analysis of FOSSD processes and socio-technical networks. Finally, there is a discussion of limitations and constraints in the FOSSD studies so far, attention to emerging opportunities for future FOSSD studies, and then conclusions about what is known about FOSSD through the empirical studies reviewed here.
Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network

It is common nowadays for e-commerce websites to encourage their users to rate shopping items and write review text. This review text information has been proven to be very useful in understanding user preferences and item properties, and thus enhances the capability of these websites to make personalized recommendations. In this paper, we propose to model user preferences and item properties using a convolutional neural network (CNN) with attention, motivated by the huge success of CNN for many natural language processing tasks. By using aggregated review text from users and items, we aim to build vector representations of user and item using attention-based CNNs. These vector representations are then used to predict rating values for a user on an item. We train these user and item networks jointly, which enables the interaction between users and items in a way similar to the matrix factorization technique. In addition, the visualization of the attention layer gives us insight on when words are selected by the models that highlight a user’s preferences or an item’s properties. We validate the proposed models on popular review datasets, Yelp and Amazon, and compare results with matrix factorization (MF), and hidden factor and topical (HFT) models. Our experiments show improvement over HFT, which proves the effectiveness of these representations learned from our networks on review text for rating prediction.
Cortical demyelination and diffuse white matter injury in multiple sclerosis.

Focal demyelinated plaques in white matter, which are the hallmark of multiple sclerosis pathology, only partially explain the patient's clinical deficits. We thus analysed global brain pathology in multiple sclerosis, focusing on the normal-appearing white matter (NAWM) and the cortex. Autopsy tissue from 52 multiple sclerosis patients (acute, relapsing-remitting, primary and secondary progressive multiple sclerosis) and from 30 controls was analysed using quantitative morphological techniques. New and active focal inflammatory demyelinating lesions in the white matter were mainly present in patients with acute and relapsing multiple sclerosis, while diffuse injury of the NAWM and cortical demyelination were characteristic hallmarks of primary and secondary progressive multiple sclerosis. Cortical demyelination and injury of the NAWM, reflected by diffuse axonal injury with profound microglia activation, occurred on the background of a global inflammatory response in the whole brain and meninges. There was only a marginal correlation between focal lesion load in the white matter and diffuse white matter injury, or cortical pathology, respectively. Our data suggest that multiple sclerosis starts as a focal inflammatory disease of the CNS, which gives rise to circumscribed demyelinated plaques in the white matter. With chronicity, diffuse inflammation accumulates throughout the whole brain, and is associated with slowly progressive axonal injury in the NAWM and cortical demyelination.
Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry- and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification scheme's feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics.
SRT 除算に基づくスケーラブル剰余乗算回路 A Scalable Modular Multiplication Hardware Based on High-radix SRT Division Algorithm

べき剰余演算は RSA暗号 [1]を始めとする公開鍵暗 号の基本演算である.このべき剰余演算は剰余乗算に分 解できる.演算のビット数が非常に大きく,処理時間が 長いことから,高速化のために多くの回路構成法が研究 されている.剰余乗算アルゴリズム [2]の中で,回路化 に有効なものとして,乗/除算をインタリーブする SRT 除算に基づく方法 [3]と,モンゴメリ乗算に基づく方法 [4]がある.後者は幾つかの前処理と後処理が必要にな るが,法の倍数の選択が容易になる.そのため,基本的 な基数 2の構成同士の比較では前者に比べて 2倍程度高 速であるとされる [5].このことから,近年は,モンゴ メリ乗算に基づく回路構成の研究が多い. また,近年は,モンゴメリ乗算に基づくスケーラブル な回路構成に関する研究が多い [6, 7, 8].これらは,ワー ド単位に処理することで,以前のものに比べてバス幅を 小さくして,より小さな回路量で実現できる.スマート カードなどの小型の装置では,従来の構成法では回路が 比較的大きいため,このような回路規模の小さい構成が 多少速度は劣るが有効となる. 本論文では,モンゴメリ乗算ではなく,高基数 SRT除 算 [9]に基づくスケーラブルな回路構成法を提案し,こ の構成の面積/速度の性能がモンゴメリ乗算に基づく構 成に匹敵することを示す.また,法の倍数選択には,高
Assessing Metacognitive Awareness during Problem-Solving in a Kinetics and Homogeneous Reactor Design Course

Since practicing engineers are hired, retained, and rewarded for solving problems, engineering students should learn how to solve workplace problems . Therefore, we designed and implemented several problem-solving learning environments (PSLEs) for the junior course entitled Kinetics and Homogeneous Reactor Design at Universidad de las Américas Puebla. Metacognition has been shown to be important for the solution of more open-ended and wellstructured problems. Flavell 5 distinguished two characteristics of metacognition: knowledge of cognition (KC) and regulation of cognition (RC). In order to support student metacognitive processing while learning to solve kinetics and homogeneous reactor design problems, the instructor created a supportive social environment in the course and inserted a series of question prompts during PSLEs, as a form of coaching where the problem to be solved was represented as a case, and cases were used in various ways (worked examples, case studies, structural analogues, prior experiences, alternative perspectives, and simulations) as instructional supports. The Metacognitive Awareness Inventory (MAI) designed by Schraw and Dennison was utilized as a pre(first day of classes) post(last day of classes) test. MAI is a 52-item inventory to measure adults’ metacognitive awareness. Items are classified into eight subcomponents subsumed under two broader categories, KC and RC. Furthermore, in order to assess metacognitive awareness during problem-solving activities, students had to answer the corresponding problem as well as approximately 2-3 embedded problem-solving prompts (from Jonassen) and 4-6 embedded metacognitive prompts (from MAI). Results for the pre-post MAI exhibited a significant (p<0.05) increase in student metacognitive awareness. This increase was also noticed by means of the embedded MAI prompts while solving different kinds of problems (such as story problems, decision-making problems, troubleshooting, and design problems) throughout the course, in which students also improved the quality of their embedded problem-solving answers and corresponding grades. Promoting metacognitive awareness and skills could be a valuable method for improving learning and student performance during kinetics and homogeneous reactor design problemsolving, as has been previously reported for professional educators and dental hygiene students.
DeepWear: a Case Study of Collaborative Design between Human and Artificial Intelligence

Deep neural networks (DNNs) applications are now increasingly pervasive and powerful. However, fashion designers are lagging behind in leveraging this increasingly common technology. DNNs are not yet a standard part of fashion de sign practice, either clothes patterns or prototyping tools. In this paper, we present DeepWear, a method using deep convolutional generative adversarial networks for clothes design. The DNNs learn the feature of specific brand clothes and generate images then patterns instructed from the images are made, and an author creates clothes based on that. We evaluated this system by evaluating the credibility of the actual sold clothes on market with our clothes. As the result, we found it is possible to make clothes look like actual products from the generated images. Our findings have implications for collaborative design between machine and human intelligence.
An Organizational Theoretic Review of Green Supply Chain Management Literature

Green supply chain management (GSCM) has gained increasing attention within both academia and industry. As the literature grows, finding new directions by critically evaluating the research and identifying future directions becomes important in advancing knowledge for the field. Using organizational theories to help categorize the literature provides opportunities to address both the objectives of understanding where the field currently stands and identifying research opportunities and directions. After providing a background discussion on GSCM we categorize and review recent GSCM literature under nine broad organizational theories. Within this review framework, we also identify GSCM research questions that are worthy of investigation. Additional organizational theories which are considered valuable for future GSCM research are also identified with a conclusion for this review.