Reviewerly/exHarmony huggingface dataset:
AmbiPlant - Ambient Feedback for Digital Media through Actuated Plants

To enhance viewing experiences during digital media consumption, both research and industry have considered ambient feedback effects to visually and physically extend the content presented. In this paper, we present AmbiPlant, a system using support structures for plants as interfaces for providing ambient effects during digital media consumption. In our concept, the media content presented to the viewer is augmented with visual actuation of the plant structures in order to enhance the viewing experience. We report on the results of a user study comparing our AmbiPlant condition to a condition with ambient lighting and a condition without ambient effects. Our system outperformed the no ambient effects condition in terms of engagement, entertainment, excitement and innovation and the ambient lighting condition in terms of excitement and innovation.
Recommender Systems Using Collaborative Tagging

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.
Digital Rock Core Images Resolution Enhancement with Improved Super Resolution Convolutional Neural Networks

It has proved that Super Resolution Convolutional Neural Networks (SRCNN) can enhance the resolution of images by other researchers. We applied SRCNN to enhance Digital Rock Core Images that play an important role in analyzing rock core in petroleum industry. We noticed that there are a large number of similarly colored patches in Digital Rock Core Images. Then we realized if we applied the original SRCNN to our project directly, we would waste some time in the first step of the SRCNN which is to establish a data set with the bicubic interpolation algorithm. This is because the results are almost the same if we use the bilinear interpolation algorithm to replace the bicubic interpolation algorithm to deal with pixels in the similarly colored patches, and the time complexity of bilinear interpolation algorithm is smaller than that of the bicubic interpolation algorithm. In order to save time, we proposed an improved SRCNN and tested the performance with 2760 Digital Rock Core Images. Experiments show that the improved SRCNN reduces 11.1% of computation time for preparing data set, but the performance of Super Resolution is similar to the original SRCNN. The proposed method can save many electricity bills when we deal with millions Digital Rock Core Images for future job. In addition, it has practical value for Super Resolution tasks with SRCNN in other fields.
Spur: Mitigating Slow Instances in Large-Scale Streaming Pipelines

Bing's monetization pipeline is one of the largest and most critical streaming workloads deployed in Microsoft's internal data lake. The pipeline runs 24/7 at a scale of 3500 YARN containers and is required to meet a Service Level Objective (SLO) of low tail latency. In this paper, we highlight some of the unique challenges imposed by this large scale of operation: other concurrent workloads sharing the cluster may cause random performance deterioration; unavailability of external dependencies may cause temporary stalls in the pipeline; scarcity in the underlying resource manager may cause arbitrarily long delays or rejection of container allocation requests. Weathering these challenges requires specially tailored dynamic control policies that react to these issues as and when they arise. We focus on the problem of reducing the latency in the tail, i.e., 99th percentile (p99), by detecting and mitigating slow instances through speculative replication. We show that widely used approaches do not satisfactorily solve this issue at our scale. A conservative approach is hesitant to acquire additional resources, reacts too slowly to the changes in the environment and therefore achieves little improvement in p99 latency. On the other hand, an aggressive approach overwhelms the underlying resource manager with unnecessary resource requests and paradoxically worsens the p99 latency. Our proposed approach, Spur, is designed for this challenging environment. It combines aggressive detection of slow instances with smart pruning of false positives to achieve a far better trade-off between these conflicting objectives. Using only 0.5% additional resources (similar to the conservative approach), we demonstrate a 10% -38% improvement in the tail latency compared to both conservative and aggressive approaches.
Improved faster RCNN for white blood cells detection in blood smear image

The inspection result of the white blood cell count is actually a critical part of medical diagnose. In the past five years, with the application of deep learning technique in the field of recognition of white blood cell, the classification accuracy of the white blood cell has been improved dramatically, however the extraction accuracy is still low, which limits the improvement of the total accuracy. In this paper, to solve the above problems, we proposed an algorithm of white blood cell recognition based on improved Faster RCNN. We improved RPN network by optimized the parameters of the original RPN network to make it have the ability to detect the small target. The results showed that the F1-Score improved compared with original Faster RCNN on self-built data set. The results show our method can extract the adhesion cells.
An extension of the type-1 and singleton fuzzy logic system trained by scaled conjugate gradient methods for multiclass classification problems

This paper proposes an extension of the type-1 and singleton fuzzy logic system for dealing with multiclass classification problems. The proposed extension enables a fuzzy classifier to generate more than one output, thereby avoiding the use of binary decomposition strategies when multiclass classification problems are considered. Additionally, with the goal of improving classifier performance, the scaled conjugate gradient training method was applied, as well as its modified version using the differential operator R·. The effectiveness of the proposed extension was evaluated using data from the UCI Machine Learning Repository based on well-established classification metrics. The numerical results reveal a significant reduction in computational complexity when using the proposed extension compared to the traditional decomposition strategy, as well as improved convergence speed when using the scaled conjugate gradient training method.
Ingestible Sensors as Design Material for Bodily Play

Ingestible sensors are pill-like digital sensors performing sensing functions inside the human body. Such technology is becoming increasingly common in clinical uses. However, we believe there exists an opportunity to also investigate ingestible sensors as design material for bodily play to facilitate intriguing bodily experiences. This argument is inspired by a long history of utilizing the intersection of medical technologies and play to bring about intriguing bodily experiences. By designing and investigating the user experience of three playful systems around ingestible sensors, we articulate a preliminary framework showing how ingestible sensors can be used as design material to support the design of playful bodily experiences.
Live Sketchnoting Across Platforms: Exploring the Potential and Limitations of Analogue and Digital Tools

Sketchnoting is the process of creating a visual record with combined text and imagery of an event or presentation. Although analogue tools are still the most common method for sketchnoting, the use of digital tools is increasing. We conducted a study to better understand the current practices, techniques, compromises and opportunities of creating both pen&paper and digital sketchnotes. Our research combines insights from semi-structured interviews with the findings from a within-subjects observational study where ten participants created real time sketchnotes of two video presentations on both paper and digital tablet. We report our key findings, categorised into six themes: insights into sense of space; trade-offs with flexibility; choice paradox and cognitive load; matters of perception, accuracy and texture; issues around confidence; and practicalities. We discuss those findings, the potential and limitations of different methods, and implications for the design of future digital sketchnoting tools.
Rotational Cryptanalysis on MAC Algorithm Chaskey

In this paper we generalize the Markov theory with respect to a relation between two plaintexts and not their difference and apply it for rotational pairs. We perform a related-key attack over Chaskey- a lightweight MAC algorithm for 32-bit micro controllers - and find a distinguisher by using rotational probabilities. Having a message m we can forge and present a valid tag for some message under a related key with probability $$2^{-57}$$ for 8 rounds and $$2^{-86}$$ for all 12 rounds of the permutation for keys in a defined weak-key class. This attack can be extended to full key recovery with complexity $$2^{120}$$ for the full number of rounds.
Deep Supervised Hashing with Dynamic Weighting Scheme

Similarity preserving hashing methods have drawn a lot of attention lately due to their efficiency in performing approximate nearest neighbor searches on high-dimensional large-scale multimedia data. With the development of deep learning techniques, deep supervised hashing has attracted increasing attention lately. Existing deep hashing methods usually take a two-step strategy to generate hash codes. They generate continuous hash codes and then discretize them into binary hash codes. Deep hashing methods have outperformed other methods significantly, but there is still a huge quantization gap between the discrete hash codes and continuous hash codes. The quantization gap prevents existing deep hashing methods from achieving better performance. In this paper, we propose deep supervised hashing with dynamic weighting scheme (DHDW), a novel deep hashing method that narrows down the quantization gap by utilizing Hamming distance information during training. Specifically, we introduce Hamming distance and stability information into the training phase by applying a novel dynamic weighting module to all data of a mini-batch. The dynamic weighting module we present is constructed from real-time Hamming distance and stability information. Additionally, the dynamic weighting module is compatible with all existing pairwise deep supervised hashing methods. Comprehensive experiments show that DHDW outperforms existing state-of-the-art methods on several standard datasets and benchmarks, and the dynamic weighting module effectively improves the performance of deep learning to hash methods.
Temporal differential elastic wave computational metamaterials

Computational metamaterials have enabled the realization of real-time mathematical operations in spatial and time domains. Here, we present the design and experimental demonstration of time-domain differential operations based on an elastic wave computational metamaterial. For generality and universality, the linearity and the product rule for the wave-based differentiation are also verified, as well as the functionality of cascaded differentiators. We expect that acoustic computational metamaterials will enable new capabilities in signal acquisition and processing and network computing and drive new applications of the sound wave.
Metric Embedding Learning on Multi-Directional Projections

Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision—mostly driven by deep learning—have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same; nature-inspired multi-layered convolutional nets learn the representations, and the output of such a model maps them to a multidimensional encoding space. A metric based loss brings same instance embeddings close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of high parameter number, which is in a relationship with input image size. Upon shrinking the input, the model requires less trainable parameters, while performance decreases. This drawback is tackled by using compressed feature extraction, e.g., projections. In this paper, a multi-directional image projection transformation with fixed vector lengths (MDIPFL) is applied for one-shot recognition tasks, trained on Siamese and Triplet architectures. Results show, that MDIPFL based approach achieves decent performance, despite of the significantly lower number of parameters.
Importance Sampling for Online Planning under Uncertainty

The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, e.g., real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves the planning performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We present a general method for learning the importance sampling distribution and demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks.
Key Update Countermeasure for Correlation-Based Side-Channel Attacks

Side-channel analysis is a non-invasive form of attack that reveals the secret key of the cryptographic circuit by analyzing the leaked physical information. The traditional brute-force and cryptanalysis attacks target the weakness in the encryption algorithm, whereas side-channel attacks use statistical models such as differential analysis and correlation analysis on the leaked information gained from the cryptographic device during the run-time. As a non-invasive and passive attack, the side-channel attack brings a lot of difficulties for detection and defense. In this work, we propose a key update scheme as a countermeasure for power and electromagnetic analysis-based attacks on the cryptographic device. The proposed countermeasure utilizes a secure coprocessor to provide secure key generation and storage in a trusted environment. The experimental results show that the proposed key update scheme can mitigate side-channel attacks significantly.
Iso-UniK: lightweight multi-process unikernel through memory protection keys

Abstract Unikernel, specializing a minimalistic libOS with an application, is an attractive design for cloud computing. However, the Achilles’ heel of unikernel is the lack of multi-process support, which makes it less flexible and applicable. Many applications rely on the process abstraction to isolate different components. For example, Apache with the multi-processing module isolates a request handler in a process to guarantee security. Prior art tackles the problem by simulating multi-process with multiple unikernels, which is incompatible with existing cloud providers and also introduces high overhead. This paper proposes Iso-UniK, a new unikernel design enabling multi-task applications with the support of both functionality and isolation. Iso-UniK leverages a recent hardware feature, named Intel Memory Protection Key (Intel MPK), to provide lightweight and efficient isolation for multi-process in unikernel. Our design has three benefits compared with previous approaches. First, Iso-UniK does not need hypervisor support and is thus compatible with existing cloud computing platforms; second, Iso-UniK promises fast system calls with only 45 cycles; last, a process can be isolated with a flexible configuration. We have implemented a prototype based on OSv, a unikernel system supporting unmodified applications. Iso-UniK can achieve fast fork operation with only 66 μ s for multi-process applications. Our evaluation shows that the isolation and multi-process support in Iso-UniK will not damage the applications’ performance.
MetaLife: Interactive Installation Based on Liquid Metal Deformable Interfaces

Driving from the novel technology of LIME (LIquid MEtal shape-changing interfaces), MetaLife presents a series of installations for educational, entertainment, or aesthetic purposes. In this paper, we discuss how we translate LIME's interaction paradigms into design vocabulary and how we use such vocabulary to design the installation. As industry and academia become disconnected from each other in the field of tangible user interfaces, MetaLife gives an example of how we can promote the bonds between them.
PredicTaps: Latency Reduction Technique for Single-taps Based on Recognition for Single-tap or Double-tap

In general, a system with touch input waits for a certain period of time (typically 350 -- 500 ms) for a subsequent tap to determine whether the initial tap was a single tap or the first tap of a double tap. This results in latency of hundreds of milliseconds for a single-tap event. To reduce the latency, we propose a novel machine-learning-based tap recognition method called "PredicTaps". In the PredicTaps method, by using touch-event data gathered from the capacitive touch surface, the system immediately predicts whether a detected tap is a single tap or the first tap of a double tap. Then, in accordance with the prediction, the system determines whether to execute a single-tap event immediately or wait for a subsequent second tap. This paper reports the feasibility study of PredicTaps.
Texture Analysis of Mammogram Using Local Binary Pattern Method

Abstract Breast cancer is one of the types of cancer that threatens the lives of women in their 40s. Based on the statistic reports, death-rate can be reduced by early detection of breast cancer. Breast cancer detection in early stages and lessen false positives in radiologist diagnosis can be achieved by combination Computer-Aided Diagnosis (CAD) with mammography. In this work, we offer a feature extraction technique as a method to lessen false-positive in breast mass recognition. Distinguishing explicit breast masses and ordinary tissue is the objective we strive to achieve. The mini-MIAS database of mammograms was used in this paper. LBP is the method that was used to extract features from the ROI. Comprehensive detection of this method can be developed, by taking the ROI inside the ground truth, which is automatically identified in the mini-MIAS and classifier majority voting. Better sensitivity, specificity, and accuracy are observed with a logistic regression classifier.
A tale of intersection types

Intersection types have come a long way since their introduction in the Seventies. They have been exploited for characterising behaviours of λ-terms and π-calculus processes, building λ-models, verifying properties of higher-order programs, synthesising code, and enriching the expressivity of programming languages. This paper is a light overview of intersection types and some of their applications.
Assessment of Students' Data Literacy Skills in Southern Nigerian Universities

The data literacy skills of students in Southern Nigeria institutions of higher learning were evaluated in this study using descriptive survey method.A total of five (5) universities were selected for this study.These comprised of 150,055 students of which 2550 constituted sample for the study.Multi-stage sampling procedure was employed.The instrument used for data collection for the study was Data Literacy Questionnaire (DLQ).The reliability of DLQ was established using Cronbach-alpha method and it yielded a reliability coefficient of 0.901.The data collected was statistical analyzed using mean and standard deviation for the research questions while t-test and Analysis of variance were used to analyze the hypotheses.Findings revealed that the students possessed moderate level of data literacy skills.The students had their lowest rating in data analysis skills.The findings also revealed that Ph.D. students had better data literacy skills compared to M.Sc.and B.Sc. students, while M.Sc students' had better data skills compared to B.Sc. students.The study recommends that programs and workshops should be designed to help improve data skills for teachers and students in order to meet global standards.
A machine learning color-based segmentation for object detection within dual X-ray baggage images

Billions of suitcases and other belongings are checked every year in the X-ray systems of airports around the world. This process is of great importance because it involves the detection of possible dangerous objects such as weapons or explosives. However, the work done by airport surveillance personnel is not free from errors usually due to tiredness or distractions. This is a security problem that can always be reduced with the help of automatic intelligent tools. This paper proposes a machine learning (ML) application for image segmentation. First, it is used a color-based pixel segmentation of images to separate organic, inorganic, mixed and opaque objects from the background. Second, those five types of images are reduced in the so-called fusion phase and classified into only two: organic and inorganic. A comparative study of several ML algorithms with heuristics over a large data set of X-ray images is presented for the classification of organic and inorganic objects for a future dangerous object detection work.
Evaluation of CMAF in live streaming scenarios

HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces significant end to end latency compared to traditional broadcast, due to the the client requiring a large enough buffer for the ABR algorithm to react to changes in network conditions in a timely manner. The recently standardised Common Media Application Format (CMAF) has helped address the issue of latency by defining segments as composed of independently transferable chunks. In this paper, we describe a simulation model we have developed to evaluate the performance of four popular ABR algorithms using DASH and CMAF in various low latency live streaming scenarios. Realistic network conditions are used for the evaluation, which are based on throughput data taken from the CDN logs of a commercial live TV service. We quantify the performance of the ABR algorithms using a selection of QoE metrics, and show that CMAF can significantly improve ABR performance in low delay scenarios.