Reviewerly/exHarmony huggingface dataset:
Stylus knife

In this paper, we propose an interactive system for controlling the pressure while cutting paper with a knife. Our system supports skill improvement for novices by measuring and evaluating their cutting pressure in real time. In this study, we use a knife with a blade attached to a stylus with a pressure sensitive sensor, which can measure the pressure, coordinates, and cutting time. We compare the cutting pressure values of novices with those of artists and thereby observe the difference in their cutting skills. We have also developed a practice system that guides the novices on controlling the pressure by providing information on the cutting pressure values of experts. This system shows the difference in pressure between novices and experts using a synchronous display of color and sound. Using these functions, novices learn to adjust their cutting pressure according to that of experts. The purpose of this research is to improve the cutting skill of novices learning the art of paper-cutting. Instructors typically teach the novices how to control the cutting pressure during paper-cutting workshops, as novices tend to cut paper with an unstable and higher pressure than necessary. Determining the right cutting pressure is a critical skill in the art of paper-cutting, and we aim to improve the same with our system. We verify the effect of our system by practicing paper-cutting in various methods and comparing the cutting pressure values obtained. It is evident from the results that novices who practice with our system show progress on the average and variance of cutting pressures.
Is it safe to dockerize my database benchmark?

Docker seems to be an attractive solution for cloud database benchmarking as it simplifies the setup process through pre-built images that are portable and simple to maintain. However, the usage of Docker for benchmarking is only valid if there is no effect on measurement results. Existing work has so far only focused on the performance overheads that Docker directly induces for specific applications. In this paper, we have studied indirect effects of dockerization on the results of database benchmarking. Among others, our results clearly show that containerization has a measurable and non-constant influence on measurement results and should, hence, only be used after careful analysis.
"Jack-of-All-Trades"

A growing number of conversational agents are being embedded into larger systems such as smart homes. However, little attention has been paid to the user interactions with conversational agents in the multi-device collaboration context (MDCC), where a multiple number of devices are connected to accomplish a common mission. The objective of this study is to identify the roles of conversational agents in the MDCC. Toward this goal, we conducted semi-structured interviews with nine participants who are heavy users of smart speakers connected with home IoT devices. We collected 107 rules (usage instances) and asked benefits and limitations of using those rules. Our thematic analysis has found that, while the smart speakers perform the role of voice controller in the single device context, their role extended to automation hub, reporter, and companion in the MDCC. Based on the findings, we provide design implications for smart speakers in the MDCC.
Signal Selection Methods for Efficient Multi-Target Correction

This paper proposes methods to get a minimally rectified logic circuit equivalent to a new specification. One of the proposed methods can deal with multiple target signals to be modified at the same time. Moreover, a graph-cut based method is proposed to reduce the search space. In the case the problem is difficult to be handled by the at-the-same-time method, an enhanced method is proposed to efficiently deal with the target signals one by one. The quality of results on ICCAD'17 contest benchmarks is equally good or better, average 16 times better score, than the previous work.
Morphing Design for Socially Interactive Autonomous Car by Multi-Material 3D-Printing

An autonomous car, also known as a robot car, self-driving car, is a vehicle that is capable of sensing its environment and moving with little or no human control. In most cases today, a driver can switch "driving mode" of his/her car between manual and autonomous. However, while the mode can be smartly changed, it is not able to show the current driving mode to nearby pedestrians. This might become a source of anxieties for many ordinary people living in the era of autonomous car. To overcome this issue, we propose to create a car with new expressive functions, and make it communicable and interactive. Unlike conventional approaches which use displays and LEDs, our approach is to develop 3D shape-transforming, morphable body of a car by using multi-material 3D-printing. Our first trial with printed soft membranes was successful in representing three different modes of a car seamlessly. In this paper, we introduce our concept, core technologies and implementations, and discuss further possibilities and future works.
Decision tree-based feature ranking in concept drifting data streams

Data stream mining targets the learning of predictive models that evolve over time according to changes in arriving data. Throughout the years, several approaches have been tailored to create and continuously update predictive models from these streams, and from these, Hoeffding Trees became a popular choice for learning decision trees from data streams. In this paper, we aim at quantifying and expressing the importance of features in dynamic scenarios is of the utmost importance as they allow domain experts to back up, or invalidate, a predictive model. Therefore, we propose and assess a positional gain method tailored for for both individual and ensembles of Hoeffding Trees and how these behave in both synthetic and real-world scenarios.
How Performing an Activity Makes Meaning

The analysis of tasks and workflows is a longstanding tradition in Human-Computer Interaction (HCI). In many cases, it provides a crucial basis for the usable design of interactive systems. However, established tools almost exclusively focus on task content and structure, thereby ignoring the more "experiential" aspects of task performance. To fill this gap, we combined Hierarchical Task Analysis (HTA) with the analysis of subjective accounts of meaning. Our explorative study (N=4) suggests that objective descriptions resulting from HTA and subjective experience of one and the same activity differ. People tend to subsume experientially unimportant sequences or even ignore these within their subjective experience. Furthermore, people are able to name experientially important sequences and to relate these to feelings and thoughts (i.e., meaning). In the future, more refined versions of our approach may support practitioners with the design of meaningful interaction and activities.
Effect of AI Recommendation System on the Consumer Preference Structure in e-Commerce: Based on Two types of Preference

The goal of this research is figuring out AI recommendation effects on consumer's preference by their types. In this study, two types of consumer's preference are considered including `category preference' and `attribute preference', and also types of goods are divided into manufactured products and contents by their properties such as tangibility. Two laboratory experiments will be conducted to test four hypotheses. We expect that this study gives an important message for the effective use of AI recommendation system in e-commerce platforms.
An ontology of condition assessment technologies for sewer networks

Ageing infrastructure, budgetary constraints, economic downturns, regulatory requirements, the high level of service requirements and a declining rate of reinvestment require effective asset management of sewer collection systems. Inspection and condition assessment is one of the core and costly elements of effective asset management. The availability of many sophisticated condition assessment technologies and lack of common understanding of them in the municipal environment require formalising the knowledge related to these technologies using an ontological approach to support the development of applications for effective selection of the most appropriate technology for sewer condition assessment. The purpose of this research work is to develop an ontology of condition assessment technologies for sewer networks – Cats_Onto – using the Ontology Web Language following a seven-step methodology. The outcome of this research work is a comprehensive knowledge representation in the form of an ontology that will be used to develop applications (beyond the scope of this paper) for the selection of the most appropriate technology for sewer condition assessment. The knowledge representation is verified using automated built-in reasoners in the Protégé ontology editor and validated by industry experts.
Halide and GENESIS for Generating Domain-Specific Architecture of Guided Image Filtering

Halide is a domain-specific language for image processing on CPUs and GPUs. The language is powerful for image processing with deep pipeline, e.g., guided image filtering. The guided image filtering is utilized for various applications. Some papers implement the filter on hardware; however, the implementing hardware and the purposes are different. Hardware implementation is hard; therefore, compiler supports are necessary. We utilize Halide with extended FPGA backend, called GENESIS. In our experiment, Halide code with CPU/FPGA backend is faster than the optimized C++. Also, the code length of the C++ and Halide for CPU/FPGA is 575, 141, 139 lines, respectively.
Offline Handwritten Quranic Text Recognition: A Research Perspective

This paper aims to explain the recognition system for the offline handwritten Quranic text. The Arabic is inherent language for Quranic text. The Arabic handwritten text differs from others in writing style, use of diacritics, ligatures and overlaps. These make the process of recognition handwritten Quranic text more challenging. An overview of the recognition system as well as, the associated problems in recognition of handwritten Quranic Arabic text is presented.
Multiple Object Detection Mechanism Using YOLO

Object detection is a computer technology which relates with image processing and computer technology. There are many cases where in a given situation there is a need for faster object detection. For example, consider a traffic scenario or a case of natural disaster. In such areas, the detection of humans or specified objects becomes difficult. In such cases, there is a need for the better and quicker detection mechanism. Thus, in this paper, we proposed the YOLO detection technique. This technique uses the concept of deep neural networks and helps in the faster recognition of humans and objects.
Chain code compression with modified interpolative coding

A lossless chain code compression algorithm consisting of Burrows-Wheeler Transform, Move-To-Front Transform, and a modified interpolative coding is presented in this paper. The interpolative coding divides a strictly increasing array of integers and encodes the middle element recursively, where the required number of bits is obtained from the difference of the array’s border values. In this paper, it is shown that, for some intervals, Golomb coding is more efficient than the interpolative coding. This fact was used to construct a hybrid approach that yields slightly better compression results than the state-of-the-art while compressing Three OrThogonal chain code, Freeman chain code in four and eight directions, and Vertex Chain Code.
A Family of $M$ -Ary $\sigma$ -Sequences With Good Autocorrelation

In this letter, we propose a family of almost M-ary σ-sequences with good autocorrelation based on cyclotomic classes. The out-of-phase autocorrelation magnitude of these M-ary sequences is upper bounded by 3 or √5.
Recurrent out-of-vocabulary word detection based on distribution of features

The repeated use of out-of-vocabulary (OOV) words in a spoken document seriously degrades a speech recognizer performance. Even though such recurrent OOV words are often important keywords in a spoken document, they are never correctly recognized. We propose a novel method for robustly detecting recurrent OOV words, which focuses on the degree of consistency among them. It first detects recurrent segments, that is recurrent phoneme sub-sequence in the output of a phoneme sequence decoder. Then, we measure the degree of consistency by using the mean and variance (distribution) of features (DOF) derived from the recurrent segments, and use our DOF for IV/OOV classification. Experiments on academic lectures illustrate that the proposed DOF-based method can robustly detect recurrent OOV words in spontaneous speech and achieves over 60% relative reduction in false alarms. It is also confirmed that detection performance improves as the OOV words are repeated more often.
Multivariate Approach to Alcohol Detection in Drivers by Sensors and Artificial Vision

This work presents a system for detecting excess alcohol in drivers to reduce road traffic accidents. To do so, criteria such as alcohol concentration the environment, a facial temperature of the driver and width of the pupil are considered. To measure the corresponding variables, the data acquisition procedure uses sensors and artificial vision. Subsequently, data analysis is performed into stages for prototype selection and supervised classification algorithms. Accordingly, the acquired data can be stored and processed in a system with low-computational resources. As a remarkable result, the amount of training samples is significantly reduced, while an admissible classification performance is achieved - reaching then suitable settings regarding the given device’s conditions.
GCache: Neighborhood-Guided Graph Caching in a Distributed Environment

Distributed graph systems are becoming extremely popular due to their flexibility, scalability, and robustness in big graph processing. In order to improve the performance of the distributed graph systems, caching is a very effective technique to achieve fast response and reduce the communication cost. Existing works include online and offline caching algorithms. Online caching algorithms (such as least recently used (LRU) and most recently used (MRU)) are lightweight and flexible, however, neglect the topological properties of big graphs. Offline caching algorithms (such as node pre-ordered) consider the graph topology, but are very expensive and heavy. In this paper, we propose a novel caching mechanism, GraphCache (GCache), for big distributed graphs. GCache consists of an offline phase and an online phase, which inherits the advantages of online and offline caching algorithms. Specifically, the offline phase provides a caching model based on the bipartite graph clustering and give efficient algorithms to solve it. The online phase caches and schedules the graph clusters output from the offline phase, based on the LRU and MRU strategies. GCache can be seamlessly integrated into the state-of-the-art graph processing systems, e.g., Giraph. Finally, our experimental results demonstrate the feasibility of our proposed caching techniques in speeding up graph algorithms over distributed big graphs.
Deep-Learning Inferencing with High-Performance Hardware Accelerators

In order to improve their performance-per-watt capabilities over general-purpose architectures, FPGAs are commonly employed to accelerate applications. With the exponential growth of available data, machine-learning apps have generated greater interest in order to more comprehensively understand that data and increase autonomous processing. As FPGAs become more readily available on cloud services like Amazon Web Services F1 platform, it is worth studying the performance of accelerating machine-learning apps on FPGAs over traditional fixed-logic devices, like CPUs and GPUs. FPGA frameworks for accelerating convolutional neural networks (CNN), which are used in many machine-learning apps, have begun to emerge for accelerated-application development. This research aims to compare the performance of these forthcoming frameworks on two commonly used CNNs, GoogLeNet and AlexNet. Specifically, handwritten Chinese character recognition is benchmarked across multiple FPGA frameworks on Xilinx and Intel FPGAs and compared against multiple CPU and GPU architectures featured on AWS, Google's Cloud platform, the University of Pittsburgh's Center for Research Computing (CRC), and Intel's vLab Academic Cluster. All NVIDIA GPUs have proven to have the best performance over every other device in this study. The Zebra framework available for Xilinx FPGAs showed to have an average 8.3 times and 9.3 times performance and efficiency improvement, respectively, over the OpenVINO framework available for Intel FPGAs. Although the Zebra framework on the Xilinx VU9P showed greater efficiency than the Pascal-based GPUs, the NVIDIA Tesla V100 proved to be the most efficient device at 125.9 and 47.2 images-per-second-per-Watt for AlexNet and GoogLeNet, respectively. Although currently lacking, FPGA frameworks and devices have the potential to compete with GPUs in terms of performance and efficiency.
Project-Based Learning Inspires Cybersecurity Students: A Scholarship-for-Service Research Study

Over four summer days in 2017, cybersecurity students at the University of Maryland, Baltimore County (UMBC) analyzed the security of a targeted portion of the UMBC campus network, discovering numerous flaws, creating proof-of-concept exploits, and providing practical recommendations for mitigation. We report on this novel summer research study; its technical findings; and takeaways for students, educators, and Information Technology Departments.
Experimental results in bearings-only tracking using the sequential Monte-Carlo probability hypothesis density filter

We evaluate the use of a probability hypothesis density (PHD) filter in a bearings-only tracking application. The main feature of a PHD filter is that it propagates the first-order statistical moment of a multisource posterior distribution. Multisource estimation using a PHD filter has been shown to reliably track multiple simulated targets in the bearings-only case. In this paper we evaluate the utility of the sequential Monte-Carlo PHD filter for tracking surface ships using bearings-only data acquired from a Bluefin-21 unmanned underwater vehicle in Boston Harbor. The unmanned underwater vehicle was equipped with a rigidly mounted planar hydrophone array that measures the bearing angle to sources of acoustic noise, of which shipping traffic is the dominant source. We further evaluate several target maneuvering models, including clockwise and counter-clockwise coordinated turns. The combination of the coordinated turn models with a constant velocity model is used in a multiple model PHD filter. The results of the multiple model PHD filter are compared to the results of a PHD filter using only a constant velocity model.
An improved image enhancement method based on lab color space retinex algorithm

Due to the lack of color and “halo artifacts” in enhancement of low illuminance images based on existing algorithms, this paper presents an image enhancement algorithm based on an improved Lab color space Retinex algorithm. The author innovates the image preprocessing, improves the guided filtering considering the deviation of the guided filtering algorithm, and proposes a new method to enhance the filtered image. The overall idea of image enhancement in this paper is as follows. First of all, the low illumination image is preprocessed so that the obvious dark regions in the image are roughly enhanced and adjusted. Then the preprocessed image is converted to Lab color space and the luminance component (L) and chroma components (a, b) are separated out. Using a modified guided filtering algorithm to estimate the incident component luminance value to obtain the reflected component, after the gamma correction is performed, the image brightness contrast is improved by the adaptive logarithm mapping method. Finally, the brightness component and chromaticity component are recombined and converted to RGB color space. Experiments show that the improved algorithm proposed in this paper has a better image enhancement effect and can solve the "halo artifact" problem of the image.
LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks

Anomaly detection can show significant behavior changes in the cellular mobile network. It can explain much important missing information and which can be monitored using advanced AI (Artificial Intelligent) applications/tools. In this paper, we have proposed LSTM (Long Short-Term Memory) based RNN (Recurrent Neural Network) which can model a time series profile for LTE network based on cell KPI values. We have shown in this paper that the dynamic behavior of a single cell can be simplified using a combination of a set for neighbor cells. We can predict the profile and anomalous behavior using this method. According to the best of our knowledge this approach is applied here for the first time for cell level performance profile generation and anomaly detection. In a related work, they have proposed ensemble method to compare different KPIs and cell performance using machine learning algorithm. We have applied DNN (Deep Neural Network) to generate a profile on KPI features from historical data. It gave us deeper insight into how the cell is performing over time and can connect with the root causes or hidden fault of a major failure in the cellular network.
BA2Cs: Bounded abstaining with two constraints of reject rates in binary classification

Abstaining classifiers refrain from uncertain classifications to decrease errors and have been widely applied in critical fields. There are two types of abstaining classifiers: Chow’s rule-based and receiver operating characteristic (ROC)-based. Chow’s rule is obtained by minimizing the overall error rate given a reject rate. Therefore, Chow’s rule-based classifiers do not consider the difference between false positives and false negatives. ROC-based abstaining classifiers minimize cost functions; therefore, costs of classification and rejection need to set during the training stage. However, in real-world applications, misclassification costs of different classes are usually unequal. Furthermore, costs of classification and rejection are not easy to obtain and they may vary over time. To overcome such drawbacks, we propose a bounded abstaining classifier with two constraints of reject rates (BA2Cs). BA2Cs aims at maximizing the area under the ROC curve (AUC) while constraining the reject rates of the positive and negative classes, respectively. Optimizing AUC allows the classifier independent from class distributions and misclassification costs. We propose an efficient algorithm, which has linear time complexity, to construct the BA2Cs classifier. The algorithm searches rejection thresholds according to an incremental moving rule until the two constraints are satisfied. Experimental results show that BA2Cs obtains higher AUC and lower cost than state-of-the-art abstaining classifiers.