nils-herrmann/clean_scirepeval huggingface dataset:
Discovering Object Functionality

Object functionality refers to the quality of an object that allows humans to perform some specific actions. It has been shown in psychology that functionality (affordance) is at least as essential as appearance in object recognition by humans. In computer vision, most previous work on functionality either assumes exactly one functionality for each object, or requires detailed annotation of human poses and objects. In this paper, we propose a weakly supervised approach to discover all possible object functionalities. Each object functionality is represented by a specific type of human-object interaction. Our method takes any possible human-object interaction into consideration, and evaluates image similarity in 3D rather than 2D in order to cluster human-object interactions more coherently. Experimental results on a dataset of people interacting with musical instruments show the effectiveness of our approach.
Recoding, storage, rehearsal and grouping in verbal short-term memory: an fMRI study

Functional magnetic resonance imaging (fMRI) of healthy volunteers is used to localise the processes involved in verbal short-term memory (VSTM) for sequences of visual stimuli. Specifically, the brain areas underlying (i) recoding, (ii) storage, (iii) rehearsal and (iv) temporal grouping are investigated. Successive subtraction of images obtained from five tasks revealed a network of left-lateralised areas, including posterior temporal regions, supramarginal gyri, Broca's area and dorsolateral premotor cortex. The results are discussed in relation to neuropsychological distinctions between recoding and rehearsal, previous neuroimaging studies of storage and rehearsal, and, in particular, a recent connectionist model of VSTM that makes explicit assumptions about the temporal organisation of rehearsal. The functional modules of this model are tentatively mapped onto the brain in light of the imaging results. Our findings are consistent with the representation of verbal item information in left posterior temporal areas and short-term storage of phonological information in left supramarginal gyrus. They also suggest that left dorsolateral premotor cortex is involved in the maintenance of temporal order, possibly as the location of a timing signal used in the rhythmic organisation of rehearsal, whereas Broca's area supports the articulatory processes required for phonological recoding of visual stimuli.
From Facial Expression Recognition to Interpersonal Relation Prediction

Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.
Is imitation learning the route to humanoid robots?

This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in the form of movement primitives. It is reviewed here how research on representations of, and functional connections between, action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning are also described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical-learning research. Parallels and differences between biological and computational approaches to imitation are highlighted and an overview of current projects that actually employ imitation learning for humanoid robots is given.
A two-view based multilayer feature graph for robot navigation

To facilitate scene understanding and robot navigation in a modern urban area, we design a multilayer feature graph (MFG) based on two views from an on-board camera. The nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D coordinate system. Building on SIFT feature points and line segments, MFG is constructed using feature fusion which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework. Physical experiments show that MFG can be successfully constructed in urban area and the construction method is demonstrated to be very robust in identifying feature correspondence.
Predictive Control of Power Converters: Designs With Guaranteed Performance

In this work, a cost function design based on Lyapunov stability concepts for finite control set model predictive control is proposed. This predictive controller design allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters. Simulation and experimental results on a buck dc-dc converter and a two-level dc-ac inverter are conducted to validate the effectiveness of our proposal.
Gradient Descent Finds Global Minima of Deep Neural Networks

Simon S. Du, Jason D. Lee*, Haochuan Li*, Liwei Wang*, and Xiyu Zhai* Machine Learning Department, Carnegie Mellon University Data Science and Operations Department, University of Southern California School of Physics, Peking University Key Laboratory of Machine Perception, MOE, School of EECS, Peking University Center for Data Science, Peking University, Beijing Institute of Big Data Research Department of EECS, Massachusetts Institute of Technology
The German Traffic Sign Recognition Benchmark: A multi-class classification competition

The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.
Survey of Standardized Protocols for the Internet of Things

Internet of Things is a growing technology, developing in multiple fields, from smart homes to health and industries. From a technological perspective, it enables the development of new protocols and scenarios, since the standard network protocol suite cannot face the growing number of connected devices and data transmitted. This paper aims to present several standardized protocols, at different networking levels, developed especially for embedded devices with low memory, low processing power and low data rate. We also propose a smart home scenario that uses only standardized IoT protocols.
Deep Learning in Radiology: Does One Size Fit All?

Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculation of other properties). Other DL networks are able to predict important properties from regions of an image-for instance, whether something is malignant, molecular markers for tissue in a region, even prognostic markers. DL is easier to train than traditional machine learning methods, but requires more data and much more care in analyzing results. It will automatically find the features of importance, but understanding what those features are can be a challenge. This article describes the basic concepts of DL systems and some of the traps that exist in building DL systems and how to identify those traps.
Reining in the Outliers in Map-Reduce Clusters using Mantri

Experience from an operational Map-Reduce cluster reveals that outliers signi cantly prolong job completion. e causes for outliers include run-time contention for processor, memory and other resources, disk failures, varying bandwidth and congestion along network paths and, imbalance in task workload. We present Mantri, a system that monitors tasks and culls outliers using causeand resource-aware techniques. Mantri’s strategies include restarting outliers, network-aware placement of tasks and protecting outputs of valuable tasks. Using real-time progress reports,Mantri detects and acts on outliers early in their lifetime. Early action frees up resources that can be used by subsequent tasks and expedites the job overall. Acting based on the causes and the resource and opportunity cost of actions lets Mantri improve over prior work that only duplicates the laggards. Deployment in Bing’s production clusters and trace-driven simulations show that Mantri improves job completion times by .
Characterizing the memory behavior of Java workloads: a structured view and opportunities for optimizations

This paper studies the memory behavior of important Java workloads used in benchmarking Java Virtual Machines (JVMs), based on instrumentation of both application and library code in a state-of-the-art JVM, and provides structured information about these workloads to help guide systems' design. We begin by characterizing the inherent memory behavior of the benchmarks, such as information on the breakup of heap accesses among different categories and on the hotness of references to fields and methods. We then provide detailed information about misses in the data TLB and caches, including the distribution of misses over different kinds of accesses and over different methods. In the process, we make interesting discoveries about TLB behavior and limitations of data prefetching schemes discussed in the literature in dealing with pointer-intensive Java codes. Throughout this paper, we develop a set of recommendations to computer architects and compiler writers on how to optimize computer systems and system software to run Java programs more efficiently. This paper also makes the first attempt to compare the characteristics of SPECjvm98 to those of a server-oriented benchmark, pBOB, and explain why the current set of SPECjvm98 benchmarks may not be adequate for a comprehensive and objective evaluation of JVMs and just-in-time (JIT) compilers.We discover that the fraction of accesses to array elements is quite significant, demonstrate that the number of "hot spots" in the benchmarks is small, and show that field reordering cannot yield significant performance gains. We also show that even a fairly large L2 data cache is not effective for many Java benchmarks. We observe that instructions used to prefetch data into the L2 data cache are often squashed because of high TLB miss rates and because the TLB does not usually have the translation information needed to prefetch the data into the L2 data cache. We also find that co-allocation of frequently used method tables can reduce the number of TLB misses and lower the cost of accessing type information block entries in virtual method calls and runtime type checking.
A high-linearity, LC-Tuned, 24-GHz T/R switch in 90-nm CMOS

This paper presents an LC-tuned, 24-GHz single-pole double-throw (SPDT) transmit/receive (T/R) switch implemented in 90-nm CMOS. The design focuses on the techniques to increase the power handling capability in the transmit (Tx) mode under 1.2-V operation. The switch achieves a measured P-1dB of 28.7 dBm, which represents the highest linearity, reported to date, for CMOS millimeter-wave T/R switches. The transmit and receive (Rx) branches employ different switch topologies to minimize the power leakage into the Rx path during Tx mode, and hence improve the linearity. To accommodate large signal swing, AC floating bias is applied using large bias resistors to all terminals of the switch devices. Triple-well devices are utilized to effectively float the substrate terminals. The switch uses a single 1.2-V digital control signal for T/R mode selection and for source/drain bias. The measured insertion loss is 3.5 dB and return loss is better than -10 dB at 24 GHz.
BYOD security challenges: control and protect your most sensitive data

According to a recent survey by Harris Interactive and ESET, more than 80% of employed adults use some kind of personally owned electronic device for work-related  One of the primary advantages in allowing employees to bring and use their personal devices for work is that it increases efficiency and flexibility. By giving access to the corporate network, and therefore corporate information via unmanaged devices, work can be done from anywhere, at any time, using any endpoint. It also provides cost savings to organisations, since personal device usage means they don’t need to provide or manage mobile devices for their employees. The Harris survey also determined that 47% of employees use personal desktop computers to access or store company information, while 41% do this with personal laptops, 24% with smartphones and 10% with tablets. However, with less than half of these devices being protected by basic security measures, some organisations may start to worry that the security challenges associated with BYOD far outweigh the benefits.
TESS: Temporal event sequence summarization

We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models. A data set of temporal event sequences can be represented as a data set of three-dimensional points, each point is defined by three variables: a sequence identifier, a time value and an event value. Instantiating data grid models to the 3D-points turns the problem into 3D-coclustering. The sequences are partitioned into clusters, the time variable is discretized into intervals and the events are partitioned into clusters. The cross-product of the univariate partitions forms a multivariate partition of the representation space, i.e., a grid of cells and it also represents a nonparametric estimator of the joint distribution of the sequences, time and events dimensions. Thus, the sequences are grouped together because they have similar joint distribution of time and events, i.e., similar distribution of events along the time dimension. The best data grid is computed using a parameter-free Bayesian model selection approach. We also suggest several criteria for exploiting the resulting grid through agglomerative hierarchies, for interpreting the clusters of sequences and characterizing their components through insightful visualizations. Extensive experiments on both synthetic and real-world data sets demonstrate that our approach is efficient, effective and discover meaningful underlying patterns in sets of temporal event sequences.
Low cost virtual reality for medical training

This demo depicts a low cost virtual reality set-up that may be used for medical training and instruction purposes. Using devices such as the Oculus Rift and Razer Hydra, an immersive experience, including hand interactivity can be given. Software running on a PC integrates these devices and presents an interactive and immersive training environment, where trainees are asked to perform a mixed bag of both, simple and complex tasks. These tasks range from identification of certain organs to performing of an actual incision. Trainees learn by doing, albeit in the virtual world. Components of the system are relatively affordable and simple to use, thereby making such a set-up incredibly easy to deploy.
Think-aloud protocols: a comparison of three think-aloud protocols for use in testing data-dissemination web sites for usability

We describe an empirical, between-subjects study on the use of think-aloud protocols in usability testing of a federal data-dissemination Web site. This double-blind study used three different types of think-aloud protocols: a traditional protocol, a speech-communication protocol, and a coaching protocol. A silent condition served as the control. Eighty participants were recruited and randomly pre-assigned to one of four conditions. Accuracy and efficiency measures were collected, and participants rated their subjective satisfaction with the site. Results show that accuracy is significantly higher in the coaching condition than in the other conditions. The traditional protocol and the speech-communication protocol are not statistically different from each other with regard to accuracy. Participants in the coaching condition are more satisfied with the Web site than participants in the traditional or speech-communication condition. In addition, there are no significant differences with respect to efficiency (time-on-task). This paper concludes with recommendations for usability practitioners.
Cholinesterase inhibitors from Sargassum and Gracilaria gracilis: seaweeds inhabiting South Indian coastal areas (Hare Island, Gulf of Mannar).

Dementia is a chronic progressive mental disorder, which adversely affects memory, thinking, comprehension, calculation and language. Some of the commonest forms of dementia are Alzheimer's disease, Parkinsonism, Dementia with Lewy Bodies and Myasthenia gravis. All these disorders are related to abnormalities in the central cholinergic system, which shows a decline in acetylcholine level. Cholinesterase (ChE) inhibitors are one of the novel strategies used for the symptomatic treatment of neurological disorders like dementia. In the course of screening new ChE inhibitors from marine sources, about 11 seaweeds, which have wide pharmaceutical applications, were collected from Hare Island, Gulf of Mannar, Tamilnadu, India. Methanolic extracts of the seaweeds were assessed for ChE inhibitory activity under in vitro conditions. Kinetic parameters IC(50), K(i) and V(max) were also analysed. The results showed that 3/11 seaweeds showed 50% inhibition for both ChEs (using acetylthiocholine iodide and butyrylthiocholine iodide as substrate) at concentrations of 2 mg mL(-1) (Gracilaria gracilis, Sargassum, Cladophora fasicularis for ChE with acetylthiocholine iodide as substrate and Gracilaria gracilis, Gracilaria edulis, Sargassum for ChE with butyrylthiocholine iodide as substrate) and 4/11 showed no inhibitory activity. Inhibitory activity of seaweed extracts was compared with standard drug donepezil. Enzyme kinetic analysis showed that algal extracts exhibited mixed type inhibition (partially non-competitive inhibition).
Beyond Bowling Together : SocioTechnical Capital

Social resources like trust and shared identity make it easier for people to work and play together. Such social resources are sometimes referred to as social capital. Thirty years ago, Americans built social capital as a side effect of participation in civic organizations and social activities, including bowling leagues. Today, they do so far less frequently (Putnam 2000). HCI researchers and practitioners need to find new ways for people to interact that will generate even more social capital than bowling together does. A new theoretical construct, SocioTechnical Capital, provides a framework for generating and evaluating technology-mediated social relations.
ImageNet Classification with Deep Convolutional Neural Networks

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Decentralizing Attribute-Based Encryption

We propose a Multi-Authority Attribute-Based Encryption (ABE) system. In our system, any party can become an authority and there is no requirement for any global coordination other than the creation of an initial set of common reference parameters. A party can simply act as an ABE authority by creating a public key and issuing private keys to different users that reflect their attributes. A user can encrypt data in terms of any boolean formula over attributes issued from any chosen set of authorities. Finally, our system does not require any central authority. In constructing our system, our largest technical hurdle is to make it collusion resistant. Prior Attribute-Based Encryption systems achieved collusion resistance when the ABE system authority “tied” together different components (representing different attributes) of a user’s private key by randomizing the key. However, in our system each component will come from a potentially different authority, where we assume no coordination between such authorities. We create new techniques to tie key components together and prevent collusion attacks between users with different global identifiers. We prove our system secure using the recent dual system encryption methodology where the security proof works by first converting the challenge ciphertext and private keys to a semi-functional form and then arguing security. We follow a recent variant of the dual system proof technique due to Lewko and Waters and build our system using bilinear groups of composite order. We prove security under similar static assumptions to the LW paper in the random oracle model.