nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Laugh (automatically): Computational Models for Humor Recognition

Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this article, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over a priori known baselines.
Towards a Privacy-Enhanced Social Networking Site

Social Networking Sites (SNS), such as Facebook and LinkedIn, have become the established place for keeping contact with old friends and meeting new acquaintances. As a result, a user leaves a big trail of personal information about him and his friends on the SNS, sometimes even without being aware of it. This information can lead to privacy drifts such as damaging his reputation and credibility, security risks (for instance identity theft) and profiling risks. In this paper, we first highlight some privacy issues raised by the growing development of SNS and identify clearly three privacy risks. While it may seem a priori that privacy and SNS are two antagonist concepts, we also identified some privacy criteria that SNS could fulfill in order to be more respectful of the privacy of their users. Finally, we introduce the concept of a Privacy-enhanced Social Networking Site (PSNS) and we describe Privacy Watch, our first implementation of a PSNS.
Incentive Compatibility of Bitcoin Mining Pool Reward Functions

In this paper we introduce a game-theoretic model for reward functions in Bitcoin mining pools. Our model consists only of an unordered history of reported shares and gives participating miners the strategy choices of either reporting or delaying when they discover a share or full solution. We defined a precise condition for incentive compatibility to ensure miners strategy choices optimize the welfare of the pool as a whole. With this definition we show that proportional mining rewards are not incentive compatible in this model. We introduce and analyze a novel reward function which is incentive compatible in this model. Finally we show that the popular reward function pay-per-last-N-shares is also incentive compatible in a more general model.
Hardware Implementation and Performance Analysis of a Current-Sensor-Free Single Cell MPPT for High Performance Vehicle Solar Arrays

A maximum power point tracker has been previously developed for the single high performance triple junction solar cell for hybrid and electric vehicle applications. The maximum power point tracking (MPPT) control method is based on the incremental conductance (IncCond) but removes the need for current sensors. This paper presents the hardware implementation of the maximum power point tracker. Significant efforts have been made to reduce the size to 18 mm times 21 mm (0.71 in times 0.83 in) and the cost to close to $5 US. This allows the MPPT hardware to be integrable with a single solar cell. Precision calorimetry measurements are employed to establish the converter power loss and confirm that an efficiency of 96.2% has been achieved for the 650-mW converter with 20-kHz switching frequency. Finally, both the static and the dynamic tests are conducted to evaluate the tracking performances of the MPPT hardware. The experimental results verify a tracking efficiency higher than 95% under three different insolation levels and a power loss less than 5% of the available cell power under instantaneous step changes between three insolation levels.
Deep Learning for Recommender Systems

Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language processing and speech recognition. After its relatively slow uptake by the recommender systems community, deep learning for recommender systems became widely popular in 2016.
 We believe that a tutorial on the topic of deep learning will do its share to further popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the tutorial is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems.
Combining Multimodal Features within a Fusion Network for Emotion Recognition in the Wild

In this paper, we describe our work in the third Emotion Recognition in the Wild (EmotiW 2015) Challenge. For each video clip, we extract MSDF, LBP-TOP, HOG, LPQ-TOP and acoustic features to recognize the emotions of film characters. For the static facial expression recognition based on video frame, we extract MSDF, DCNN and RCNN features. We train linear SVM classifiers for these kinds of features on the AFEW and SFEW dataset, and we propose a novel fusion network to combine all the extracted features at decision level. The final achievement we gained is 51.02% on the AFEW testing set and 51.08% on the SFEW testing set, which are much better than the baseline recognition rate of 39.33% and 39.13%.
Two subtypes of narcissistic personality disorder.

The spectrum of narcissistic disturbances described in the psychoanalytic literature is not reflected in the rather narrow criteria of the third revised edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R, American Psychiatric Association, 1987). Narcissistic personality disorder can be conceptualized as occurring on a continuum between two extremes. At one end of the continuum is the oblivious subtype, and on the other end is the hypervigilant subtype. These two entities may be distinguished by characteristic transference and countertransference patterns.
ISSAQ: An Integrated Sensing Systems for Real-Time Indoor Air Quality Monitoring

With growing transportation and population density, increasing global warming and sudden climate change, air quality is one of the critical measures that is needed to be monitored closely on a real-time basis in today's urban ecosystems. This paper examines the issues, infrastructure, information processing, and challenges of designing and implementing an integrated sensing system for real-time indoor air quality monitoring. The system aims to detect the level of seven gases, ozone (O3), particulate matter, carbon monoxide (CO), nitrogen oxides (NO2), sulfur dioxide (SO2), volatile organic compound, and carbon dioxide (CO2), on a real-time basis and provides overall air quality alert timely. Experiments are conducted to validate and support the development of the system for real-time monitoring and alerting.
High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction

Feature extraction is an essential part in applications that require computer vision to recognize objects in an image processed. To extract the features robustly, feature extraction algorithms are often very demanding in computation so that the performance achieved by pure software is far from real-time. Among those feature extraction algorithms, scale-invariant feature transform (SIFT) has gained a lot of popularity recently. In this paper, we propose an all-hardware SIFT accelerator-the fastest of its kind to our knowledge. It consists of two interactive hardware components, one for key point identification, and the other for feature descriptor generation. We successfully developed a segment buffer scheme that could not only feed data to the computing modules in a data-streaming manner, but also reduce about 50% memory requirement than a previous work. With a parallel architecture incorporating a three-stage pipeline, the processing time of the key point identification is only 3.4 ms for one video graphics array (VGA) image. Taking also into account the feature descriptor generation part, the overall SIFT processing time for a VGA image can be kept within 33 ms (to support real-time operation) when the number of feature points to be extracted is fewer than 890.
Automatic Accident Detection and reporting framework for two wheelers

Technology must work for human race and improve the way help reaches a person in distress in the shortest possible time. In a developing nation like India, with the advancement in the transportation technology and rise in the total number of vehicles, road accidents are increasing at an alarming rate. If an accident occurs, the victim's survival rate increases when you give immediate medical assistance. You can give medical assistance to an accident victim only when you know the exact location of the accident. This paper presents an inexpensive but intelligent framework that can identify and report an accident for two-wheelers. This paper targets two-wheelers because the mortality ratio is highest in two-wheeler accidents in India. This framework includes a microcontroller-based low-cost Accident Detection Unit (ADU) that contains a GPS positioning system and a GSM modem to sense and generate accidental events to a centralized server. The ADU calculates acceleration along with ground clearance of the vehicle to identify the accidental situation. On detecting an accident, ADU sends accident detection parameters, GPS coordinates, and the current time to the Accident Detection Server (ADS). ADS maintain information on the movement of the vehicle according to the historical data, current data, and the rules that you configure in the system. If an accident occurs, ADS notifies the emergency services and the preconfigured mobile numbers for the vehicle that contains this unit.
A gradient descent rule for spiking neurons emitting multiple spikes

A supervised learning rule for Spiking Neural Networks (SNNs) is presented that can cope with neurons that spike multiple times. The rule is developed by extending the existing SpikeProp algorithm which could only be used for one spike per neuron. The problem caused by the discontinuity in the spike process is counteracted with a simple but effective rule, which makes the learning process more efficient. Our learning rule is successfully tested on a classification task of Poisson spike trains. We also applied the algorithm on a temporal version of the XOR problem and show that it is possible to learn this classical problem using only one spiking neuron making use of a hairtrigger situation.
Computational creativity for culinary recipes

Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. This demonstration shows a computational system that creates flavorful, novel, and perhaps healthy culinary recipes by drawing on big data techniques. It brings analytics algorithms together with disparate data sources from culinary science, chemistry, and hedonic psychophysics.
 In its most powerful manifestation, the system operates through a mixed-initiative approach to human-computer interaction via turns between human and computer. In particular, the sequential creation process is modeled after stages in human cognitive processes of creativity.
 The end result is an ingredient list, ingredient proportions, as well as a directed acyclic graph representing a partial ordering of culinary recipe steps.
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.
spatstat: An R Package for Analyzing Spatial Point Patterns

spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and ‘marks’ attached to the points of the point pattern. A unique feature of spatstat is its generic algorithm for fitting point process models to point pattern data. The interface to this algorithm is a function ppm that is strongly analogous to lm and glm. This paper is a general description of spatstat and an introduction for new users.
On-Board Detection of Pedestrian Intentions

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information.
A world's first product of three-dimensional vertical NAND Flash memory and beyond

In this work, we present a 3D 128Gb 2bit/cell vertical-NAND (V-NAND) Flash product. The use of barrier-engineered materials and gate all-around structure in the 3D V-NAND cell exhibits advantages over 1xnm planar NAND, such as small Vth shift due to small cell coupling and narrow natural Vth distribution. Also, a negative counter-pulse scheme realizes a tightly programmed cell distribution. In order to reduce the effect of a large WL coupling, a glitch-canceling discharge scheme and a pre-offset control scheme is implemented. Furthermore, an external high-voltage supply scheme along with the proper protection scheme for a high-voltage failure is used to achieve low power consumption. The chip accomplishes 50MB/s write throughput with 3K endurance for typical embedded applications. Also, extended endurance of 35K is achieved with 36MB/s of write throughput for data center and enterprise SSD applications. And 2nd generation of 3D V-NAND opens up a whole new world at SSD endurance, density and battery life for portables.
A stage-based model of personal informatics systems

People strive to obtain self-knowledge. A class of systems called personal informatics is appearing that help people collect and reflect on personal information. However, there is no comprehensive list of problems that users experience using these systems, and no guidance for making these systems more effective. To address this, we conducted surveys and interviews with people who collect and reflect on personal information. We derived a stage-based model of personal informatics systems composed of five stages (preparation, collection, integration, reflection, and action) and identified barriers in each of the stages. These stages have four essential properties: barriers cascade to later stages; they are iterative; they are user-driven and/or system-driven; and they are uni-faceted or multi-faceted. From these properties, we recommend that personal informatics systems should 1) be designed in a holistic manner across the stages; 2) allow iteration between stages; 3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and 4) explore support for associating multiple facets of people's lives to enrich the value of systems.
A 0.2 V, 480 kb Subthreshold SRAM With 1 k Cells Per Bitline for Ultra-Low-Voltage Computing

A 2 muW, 100 kHz, 480 kb subthreshold SRAM operating at 0.2 V is demonstrated in a 130 nm CMOS process. A 10-T SRAM cell allows 1 k cells per bitline by eliminating the data-dependent bitline leakage. A virtual ground replica scheme is proposed for logic "0" level tracking and optimal sensing margin in read buffers. Utilizing the strong reverse short channel effect in the subthreshold region improves cell writability and row decoder performance due to the increased current drivability at a longer channel length. The sizing method leads to an equivalent write wordline voltage boost of 70 mV and a delay improvement of 28% in the row decoder compared to the conventional sizing scheme at 0.2 V. A bitline writeback scheme was used to eliminate the pseudo-write problem in unselected columns.
Leveraging behavioral science to mitigate cyber security risk

Most efforts to improve cyber security focus primarily on incorporating new technological approaches in products and processes. However, a key element of improvement involves acknowledging the importance of human behavior when designing, building and using cyber security technology. In this survey paper, we describe why incorporating an understanding of human behavior into cyber security products and processes can lead to more effective technology. We present two examples: the first demonstrates how leveraging behavioral science leads to clear improvements, and the other illustrates how behavioral science offers the potential for significant increases in the effectiveness of cyber security. Based on feedback collected from practitioners in preliminary interviews, we narrow our focus to two important behavioral aspects: cognitive load and bias. Next, we identify proven and potential behavioral science findings that have cyber security relevance, not only related to cognitive load and bias but also to heuristics and behavioral science models. We conclude by suggesting several next steps for incorporating behavioral science findings in our technological design, development and use.
Process Control and Scheduling Issues for Multiprogrammed Shared-Memory Multiprocessors

Shared-memory multiprocessors are frequently used in a time-sharing style with multiple parallel applications executing at the same time. In such an environment, where the machine load is continuously varying, the question arises of how an application should maximize its performance while being fair to other users of the system. In this paper, we address this issue. We first show that if the number of runnable processes belonging to a parallel application significantly exceeds the effective number of physical processors executing it, its performance can be significantly degraded. We then propose a way of controlling the number of runnable processes associated with an application dynamically, to ensure good performance. The optimal number of runnable processes for each application is determined by a centralized server, and applications dynamically suspend or resume processes in order to match that number. A preliminary implementation of the proposed scheme is now running on the Encore Multimax and we show how it helps improve the performance of several applications. In some cases the improvement is more than a factor of two. We also discuss implications of the proposed scheme for multiprocessor schedulers, and how the scheme should interface with parallel programming languages.
Toward better horizontal integration among IoT services

Several divergent application protocols have been proposed for Internet of Things (IoT) solutions including CoAP, REST, XMPP, AMQP, MQTT, DDS, and others. Each protocol focuses on a specific aspect of IoT communications. The lack of a protocol that can handle the vertical market requirements of IoT applications including machine-to-machine, machine-to-server, and server-to-server communications has resulted in a fragmented market between many protocols. In turn, this fragmentation is a main hindrance in the development of new services that require the integration of multiple IoT services to unlock new capabilities and provide horizontal integration among services. In this work, after articulating the major shortcomings of the current IoT protocols, we outline a rule-based intelligent gateway that bridges the gap between existing IoT protocols to enable the efficient integration of horizontal IoT services. While this intelligent gateway enhances the gloomy picture of protocol fragmentation in the context of IoT, it does not address the root cause of this fragmentation, which lies in the inability of the current protocols to offer a wide range of QoS guarantees. To offer a solution that stems the root cause of this protocol fragmentation issue, we propose a generic IoT protocol that is flexible enough to address the IoT vertical market requirements. In this regard, we enhance the baseline MQTT protocol by allowing it to support rich QoS features by exploiting a mix of IP multicasting, intelligent broker queuing management, and traffic analytics techniques. Our initial evaluation of the lightweight enhanced MQTT protocol reveals significant improvement over the baseline protocol in terms of the delay performance.
A tactile proximity sensor

This paper introduces a novel tactile sensor with the ability to detect objects in the sensor's near proximity. For both tasks, the same capacitive sensing principle is used. The tactile part of the sensor provides a tactile sensor array enabling the sensor to gather pressure profiles of the mechanical contact area. Several tactile sensors have been developed in the past. These sensors lack the capability of detecting objects in their near proximity before a mechanical contact occurs. Therefore, we developed a tactile proximity sensor, which is able to measure the current flowing out of or even into the sensor. Measuring these currents and the exciting voltage makes a calculation of the capacitance coupled to the sensor's surface and, using more sensors of this type, the change of capacitance between the sensors possible. The sensor's mechanical design, the analog/digital signal processing and the hardware efficient demodulator structure, implemented on a FPGA, will be discussed in detail.
A fast and energy-efficient level shifter with wide shifting range from sub-threshold up to I/O voltage

This paper presents a fast and energy-efficient current mirror based level shifter with wide shifting range from sub-threshold voltage up to I/O voltage. Small delay and low power consumption are achieved by addressing the non-full output swing and charge sharing issues in the level shifter from [4]. The measurement results show that the proposed level shifter can convert from 0.21V up to 3.3V with significantly improved delay and power consumption over the existing level shifters. Compared with , the maximum reduction of delay, switching energy and leakage power are 3X, 19X, 29X respectively when converting 0.3V to a higher voltage between 0.6V and 3.3V.