Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Transmitted Speech Bandwidth on Subjective Assessments of Speaker Characteristics

The first impression of unknown speakers in telephone calls can determine our disposition towards our interlocutors and the conversation. While it has been asserted that narrowband communications (300-3,400 Hz) can severely affect perceived speech quality in comparison to wideband (50-7,000 Hz), transmission channel effects have been practically disregarded in the literature concerning the perception of speaker attributes or traits. In this study, listening tests have been conducted with 23 participants who were presented with narrowband and wideband speech and rated interpersonal speaker characteristics on a semantic differential questionnaire of 34 items. For each item, we evaluate how the subjective ratings correlate with perceived speech quality and whether they are affected by channel bandwidth, focusing on differences in speaker gender and in their warmth-attractiveness. The effects found, primarily on speakers' warmth, confidence and competence, can be explained by the alterations of pitch frequency variations caused by narrowband transmissions.
Digital Forensic in Cloud: Critical Analysis of Threats and Security in IaaS, SaaS and PaaS and Role of Cloud Service Providers

Cloud computing is inevitable in day to day working and we cannot imagine online activity without its use. Cloud services are offered at different levels in pay per use model. Its characteristics of flexibility, elasticity and reduced costs has enhanced its popularity and use amongst masses. It enable users to work by installing virtual machine thus eliminating the cost of maintaining hardware for computational work. However, important aspect which affected its use is security of users data. Cloud user and service providers are connected via Service Level Agreement. This agreement decides the terms and conditions which both the parties have to adhere to. This paper aims at critical evaluation of various services offered by service providers. It also study security threats and measures suggested by various authors.
Enabling the Disagreement among Crowds: A Collaborative Crowdsourcing Framework

Crowdsourcing is quite cheap and effective to get a data set labeled by multiple annotators in a short amount of time. Although there are many traditional methods focusing on quality control of crowdsourcing, little pays close attention to the intrinsic ambiguity in dataset, which is hard for workers to make the right decision. In this paper, we introduce DCRB, a collaborative crowdsourcing framework that can obtain high quality output even for ambiguous tasks. We enable users' disagreement and encourage them to provide in-depth explanations when the disagreement occurs. We use these explanations to analyze the ambiguous tasks and design collaborative crowdsourcing to improve the output. In addition, we also design “reward brave” incentive mechanism to encourage users' valuable explanations. The experimental results show that our method significantly improves the accuracy of crowdsourcing, especially for those ambiguous crowdsourcing tasks.
New ITU-T Standards for Gaming QoE Evaluation and Management

Measuring the quality experienced during online gaming activities is an important prerequisite for managing gaming services, but up to now no standardized methods were available for this purpose. This paper presents the results from two study items promoted by Study Group 12 of the International Telecommunication Union. Work on these items resulted in one draft Recommendation on subjective methods for evaluating gaming Quality of Experience (QoE), as well as one approved Recommendation on influence factors on gaming QoE which might be considered in service planning and management. Future work which is necessary to predict gaming QoE is addressed in the end of the paper.
To Cycle or Fight—CDK4/6 Inhibitors at the Crossroads of Anticancer Immunity

Dysregulation of cell division resulting in aberrant cell proliferation is a key hallmark of cancer, making it a rational and important target for innovative anticancer drug development. Three selective cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitors are FDA and European Medicines Agency (EMA) approved for hormone receptor–positive/HER2-negative advanced breast cancer. A major emerging appreciation is that these inhibitors not only are cytostatic, but also play critical roles in the interaction between tumor cells and the host immune response. However, to trigger an effective immune response, lymphocytes must also proliferate. This review aims to assimilate our emerging understanding on the role of CDK4/6 inhibitors in cell-cycle control, as well as their biological effect on T cells and other key immune cells, and the confluence of preclinical evidence of augmentation of anticancer immunity by these drugs. We aim to provide a framework for understanding the role of the cell cycle in anticancer immunity, discussing ongoing clinical trials evaluating this concept and challenges for developing rational combinations with immunotherapy.
Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings

The detection of overlapping speech segments is of key importance in speech applications involving analysis of multi-party conversations. The detection problem is challenging because overlapping speech segments are typically captured as short speech utterances far-field microphone recordings. In this paper, we propose detection of overlap segments using a neural network architecture consisting of long-short term memory (LSTM) models. The neural network architecture learns the presence of overlap in speech by identifying the spectrotemporal structure of overlapping speech segments. In order to evaluate the model performance, we perform experiments on simulated overlapped speech generated from the TIMIT database, and natural multi-talker conversational speech in the augmented Multiparty Interaction (AMI) meeting corpus. The proposed approach yields improvements over a Gaussian mixture model based overlap detection system. Furthermore, as an application of overlap detection, integration of overlap detection into speaker diarization task is shown to give improvement in diarization error rate.
Passive and Active Particle Damping in Soft Robotic Actuators *This work is funded by a Basic Research Grant from the University of Hong Kong.

Soft robotic actuators are highly elastic bodies that oscillate drastically once excited. This oscillation is undesirable in many applications. So far, very little studies on soft actuator damping have been reported. In this paper, we report a simple and effective vibration damping method based on passive and active particle damping. Experimental studies on the effectiveness of particle damping have been conducted. It is found that active particle damping is more effective than passive damping, nevertheless, active particle damping demands a more complicated design with extra energy source and control. Since particles are discrete matters, they can be seamless integrated into soft actuator design with only minor influence of soft actuator's compliance and softness.
Cost Functions to Specify Full-Body Motion and Multi-Goal Manipulation Tasks

While the problem of inverse kinematics on serial kinematic chains is well researched, solving motion tasks quickly on more complex robots remains an open problem. Examples include dual-arm manipulation, grasping with multi-finger hands, and full-body motion generation for humanoids. In this paper, we introduce an open-source software package for ROS and MoveIt! that solves inverse kinematics and motion tasks on robots with arbitrary kinematic trees. The underlying memetic algorithm integrates evolutionary optimization, particle swarm optimization, and gradient methods. The optimization respects joint limits, effectively avoids local minima, and achieves fast convergence to accurate solutions. More importantly, the overall motion goal is specified using a set of weighted sub-goals, providing great flexibility and control of secondary objectives. Several application examples demonstrate how to combine the predefined sub-goals to achieve complex motion tasks.
Deep Learning with Experience Ranking Convolutional Neural Network for Robot Manipulator

Supervised learning, more specifically Convolutional Neural Networks (CNN), has surpassed human ability in some visual recognition tasks such as detection of traffic signs, faces and handwritten numbers. On the other hand, even state-of-the-art reinforcement learning (RL) methods have difficulties in environments with sparse and binary rewards. They requires manually shaping reward functions, which might be challenging to come up with. These tasks, however, are trivial to human. One of the reasons that human are better learners in these tasks is that we are embedded with much prior knowledge of the world. These knowledge might be either embedded in our genes or learned from imitation - a type of supervised learning. For that reason, the best way to narrow the gap between machine and human learning ability should be to mimic how we learn so well in various tasks by a combination of RL and supervised learning. Our method, which integrates Deep Deterministic Policy Gradients and Hindsight Experience Replay (RL method specifically dealing with sparse rewards) with an experience ranking CNN, provides a significant speedup over the learning curve on simulated robotics tasks. Experience ranking allows high-reward transitions to be replayed more frequently, and therefore help learn more efficiently. Our proposed approach can also speed up learning in any other tasks that provide additional information for experience ranking.
A Quantitative Platform for Non-Line-of-Sight Imaging Problems

The computational sensing community has recently seen a surge of works on imaging beyond the direct line of sight. However, most of the reported results rely on drastically different measurement setups and algorithms, and are therefore hard to impossible to compare quantitatively. In this paper, we focus on an important class of approaches, namely those that aim to reconstruct scene properties from time-resolved optical impulse responses. We introduce a collection of reference data and quality metrics that are tailored to the most common use cases, and we deﬁne reconstruction challenges that we hope will aid the development and assessment of future methods.
The timing of fostering complementary innovation: exploring the antecedent of industry platform emergence

ABSTRACT This paper concentrates on exploring the antecedent of industry platform emergence in order to clarify the appropriate timing of opening the platform to successfully architect innovation by external complementors. An industry platform is defined as a core component of a technological system, which facilitates firms in orchestrating complementary innovation leading to the creation of network effects. Literature on industry platforms has mainly paid attention to investigating strategies that firms can implement to foster platform ecosystems. Relatively little research has been done on analysing the suitable timing of increasing platform openness. This incomplete understanding may cause confusion in the use of the industry platform concept, when deciding on the timing of encouraging complementary innovation. To explore this issue, a case study approach was employed to collecting and analysing research data. A total of five case studies were conducted. The case study findings suggest that the appropriate user acceptance of a product platform can be regarded as the antecedent of platform emergence, and this can help firms decide on the timing of opening the platform for the purpose of successfully steering complementary innovation. The research findings have made theoretical contributions to knowledge and have provided firms with practical implications.
Neural Pattern Classification Tracks Transfer-Appropriate Processing in Episodic Memory

Abstract The transfer-appropriate processing (TAP) account holds that episodic memory depends on the overlap between encoding and retrieval processing. In the current study, we employed multivariate pattern analysis (MVPA) of electroencephalography to examine the relevance of spontaneously engaged visual processing during encoding for later retrieval. Human participants encoded word-picture associations, where the picture could be a famous face, a landmark, or an object. At test, we manipulated the retrieval demands by asking participants to retrieve either visual or verbal information about the pictures. MVPA revealed classification between picture categories during early perceptual stages of encoding (∼170 ms). Importantly, these visual category-specific neural patterns were predictive of later episodic remembering, but the direction of the relationship was contingent on the particular retrieval demand of the memory task: a benefit for the visual and a cost for the verbal. A reinstatement of the category-specific neural patterns established during encoding was observed during retrieval, and again the relationship with behavior varied with retrieval demands. Reactivation of visual representations during retrieval was associated with better memory in the visual task, but with lower performance in the verbal task. Our findings support and extend the TAP account by demonstrating that processing of particular aspects during memory formation can also have detrimental effects on later episodic remembering when other aspects of the event are called-for and shed new light on encoding and retrieval interactions in episodic memory.
RADseq data reveal ancient, but not pervasive, introgression between Californian tree and scrub oak species (Quercus sect. Quercus: Fagaceae)

A long‐term debate in evolutionary biology is the extent to which reproductive isolation is a necessary element of speciation. Hybridizing plants in general are cited as evidence against this notion, and oaks specifically have been used as the classic example of species maintenance without reproductive isolation. Here, we use thousands of SNPs generated by RAD sequencing to describe the phylogeny of a set of sympatric white oak species in California and then test whether these species exhibit pervasive interspecific gene exchange. Using RAD sequencing, we first constructed a phylogeny of ten oak species found in California. Our phylogeny revealed that seven scrub oak taxa occur within one clade that diverged from a common ancestor with Q. lobata, that they comprise two subclades, and they are not monophyletic but include the widespread tree oak Q. douglasii. Next, we searched for genomic patterns of allele sharing consistent with gene flow between long‐divergent tree oaks with scrub oaks. Specifically, we utilized the D‐statistic as well as model‐based inference to compare the signature of shared alleles between two focal tree species (Q. lobata and Q. engelmannii) with multiple scrub species within the two subclades. We found that introgression is not equally pervasive between sympatric tree and scrub oak species. Instead, gene flow commonly occurs from scrub oaks to recently sympatric Q. engelmannii, but less so from scrub oaks to long‐sympatric Q. lobata. This case study illustrates the influence of ancient introgression and impact of reproductive isolating mechanisms in preventing indiscriminate interspecific gene exchange.
Consumers' Trust Maintenance: From the Perspective of Knowledge-Based and Identification-Based Trust

Trust changes and develops gradually. How to maintain and preserve trust relationship is the key problem to solve as long as initial trust is build. From the perspective of knowledge-based and identification-based trust, this article focuses on trust maintenance and its impact mechanism after transaction. Firstly, based on related literature, this paper constructs a research model of trust maintenance mechanism. Then data are gathered by the method of questionnaire. Finally, the model and hypotheses are tested by Structured Equation Model software. The results show that post-transaction trust contains knowledge-based trust and identification-based trust. Moreover, the model reveals that the former positively affects the latter. Besides, it has been confirmed that security and privacy protection and service quality affect two types of trust respectively, while familiarity affects both of them, which eventually affect satisfactory and loyalty. At last, the paper provides suggestions contributing to the development of e-commerce enterprises and points out the deficiency and the direction of future research.
Sometimes They Come Back: Testing Two Simple Hypotheses (In The Realm Of Unlabeled Data)

Consider a binary hypothesis where data are independent and identically distributed under the null hypothesis, and known only to be independent under the alternative. The statistician observes an n- vector $\mathrm{X}^{n}(n\gg 1)$ and makes a decision using the optimal likelihood ratio test. This seems a widely-known detection problem, but: What if only the set of samples of $\mathrm{X}^{n}$ are made available to the statistician, while the positions of the individual samples inside the vector are not? Does there exist an optimal test in that case? What is the fundamental performance limit? Are there nicely-performing practical detectors with affordable computational complexity? Answers to these questions are in large part unknown, despite the fact that the problem - which is becoming known under the name of unlabeled detection - is very relevant in modern sensor network applications where the sample positions can be lost due to their means of delivery from the remote units, or because of network attacks.
Aluminium Oxide (Al2O3) Nanopore Image Analysis Using Digital Image Processing Techniques

Automated microscopic image analysis serves as an efficient tool in biological studies and modern material science for qualitative analysis. The aim of current approach is to engineer an automated tool to find the effect of time on nanopore structures fabricated due to high purity electrochemical anodization of Al2O3 films in digital microscopic FESEM nanopore images. The geometrical and statistical features are used to measure the variance of nanopore size depending on the anodization properties; namely, concentration (%), time (min), temperature (°C) and voltage (V). The observations depict that the nanopore shape and size is enhanced with escalating anodization time (min) with constant concentration (%), temperature (°C) and voltage (V). It is also been revealed that, the rise in anodization time results to decline in wall thickness from 58-41nm and augment in nanopore diameter size and porosity from 32–78 nm and 0.234 to 2.034, respectively. The proposed results are interpreted and analyzed in comparison with manual results and the efficacy of the proposed method is witnessed.
Multilevel Parallelism for the Exploration of Large-Scale Graphs

We present the most recent release of our parallel implementation of the BFS and BC algorithms for the study of large scale graphs. Although our reference platform is a high-end cluster of new generation Nvidia GPUs and some of our optimizations are CUDA specific, most of our ideas can be applied to other platforms offering multiple levels of parallelism. We exploit multi level parallel processing through a hybrid programming paradigm that combines highly tuned CUDA kernels, for the computations performed by each node, and explicit data exchange through the Message Passing Interface (MPI), for the communications among nodes. The results of the numerical experiments show that the performance of our code is comparable or better with respect to other state-of-the-art solutions. For the BFS, for instance, we reach a peak performance of 200 Giga Teps on a single GPU and 5.5 Terateps on 1024 Pascal GPUs. We release our source codes both for reproducing the results and for facilitating their usage as a building block for the implementation of other algorithms.
Change-Point Detection of Gaussian Graph Signals with Partial Information

In a change-point detection problem, a sequence of signals switches from one distribution to another at an unknown time step, and the goal is to quickly and reliably detect this change. By providing new insight into signal processing and data analysis, graph signal processing promises various applications including image processing and sensor network analysis, and becomes an emerging field of research. In this work, we formulate the problem of change-point detection on graph. Under the reasonable assumption of normality, we propose a CUSUM-based algorithm for change-point detection with an arbitrary, unknown and perhaps time-varying mean shift after the change-point. We further propose a decentralized, distributed algorithm, which requires no fusion center, to reduce computational complexity, as well as costs and delays of communication. Numerical results on both synthetic and real-world data demonstrate that our algorithms are efficient and accurate.
Vision-Based Robotic Grasping and Manipulation of USB Wires

The fast expanding 3C (Computer, Communication, and Consumer electronics) manufacturing leads to a high demand on the fabrication of USB cables. While several commercial machines have been developed to automate the process of stripping and soldering of USB cables, the operation of manipulating USB wires according to the color code is heavily dependent on manual works because of the deformation property of wires, probably resulting in the falling-off or the escape of wires during manipulation. In this paper, a new vision-based controller is proposed for robotic grasping and manipulation of USB wires. A novel two-level structure is developed and embedded into the controller, where Level-I is referred to as the grasping and manipulation of wires, and Level-II is referred to as the wire alignment by following the USB color code. The proposed formulation allows the robot to automatically grasp, manipulate, and align the wires in a sequential, simultaneous, and smooth manner, and hence to deal with the deformation of wires. The dynamic stability of the closed-loop system is rigorously proved with Lyapunov methods, and experiments are performed to validate the proposed controller.
Low expression of MUC2 is associated with longer disease-free survival in patients with colorectal carcinoma

Background/Aim: The objective of this study was to investigate the relationship between MUC2 immunostaining and clinicopathological characteristics in a subset of colorectal carcinomas (CRCs). Materials and Methods: A total of 128 CRCs, 50 local nodal metastases, and 42 normal colonic mucosae were retrieved from the archives at the Department of Pathology at King Abdulaziz University, Jeddah, Saudi Arabia. Immunohistochemistry was performed using anti-MUC2 antibody. A cut-off of 25% of positive immunostaining was used to define low and high immunostaining. Statistical tests were used to determine the association of MUC2 with clinicopathological characteristics and survival. Results: MUC2 immunostaining was observed in 66.7% in normal colonic mucosa. Low MUC2 immunostaining was higher in primary CRC (P = 0.003) and nodal metastasis (80%) (P < 0.001). There was significant association of low MUC2 immunostaining in CRC with age group below 60 years (P = 0.05) and occurrence of lymphovascular invasion (P = 0.034). Other clinicopathological parameters were not correlated with MUC2 immunostaining. Regression analysis revealed that low MUC2 immunostaining was an independent predictor of lymphovascular invasion (P = 0.041). In the Kaplan–Meier survival analysis, there was a significant longer disease-free survival in patients with low MUC2 immunostaining (P = 0.045). However, there was no association between MUC2 immunostaining and overall survival (P = 0.601). Conclusion: MUC2 immunostaining may have distinct clinical significance and provide valuable information and could be considered as an important independent prognostic factor while planning the adjuvant therapy in CRC. In future perspective, characterization of MUC2 immunostaining on a large number of cases and molecular studies may be needed.