Reviewerly/exHarmony huggingface dataset:
Activity of the inferior parietal cortex is modulated by visual feedback delay in the robot hand illusion

Abstract The robot hand illusion (RoHI) is the perception of self-ownership and self-agency of a virtual (robot) hand that moves consistently with one’s own. The phenomenon shows that self-attribution can be established via temporal integration of visual and movement information. Our previous study showed that participants felt significantly greater RoHI (sense of self-ownership and sense of self-agency) when visuomotor temporal discrepancies were less than 200 ms. A weaker RoHI effect (sense of self-agency only) was observed when temporal discrepancies were between 300 and 500 ms. Here, we used functional near-infrared spectroscopy (fNIRS) to investigate brain activity associated with the RoHI under different visual feedback delays (100 ms, 400 ms, 700 ms). We found that the angular and supramarginal gyri exhibited significant activation in the 100-ms feedback condition. ANOVA indicated a significant difference between the 100-ms condition and the other conditions ( p &lt; 0.01). These results demonstrate that activity in the posterior parietal cortex was modulated by the delay between the motor command and the visual feedback of the virtual hand movements. Thus, we propose that the inferior parietal cortex is essential for integrating motor and visual information to distinguish one’s own body from others.
Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis

The aim of this study was to train and validate deep learning algorithms to quantitate relative amyloid deposition (RAD; mean amyloid deposited area per stromal area) in corneal sections from patients with familial amyloidosis, Finnish (FAF), and assess its relationship with visual acuity.Corneal specimens were obtained from 42 patients undergoing penetrating keratoplasty, stained with Congo red, and digitally scanned. Areas of amyloid deposits and areas of stromal tissue were labeled on a pixel level for training and validation. The algorithms were used to quantify RAD in each cornea, and the association of RAD with visual acuity was assessed.In the validation of the amyloid area classification, sensitivity was 86%, specificity 92%, and F-score 81. For corneal stromal area classification, sensitivity was 74%, specificity 82%, and F-score 73. There was insufficient evidence to demonstrate correlation (Spearman's rank correlation, -0.264, p = 0.091) between RAD and visual acuity (logMAR).Deep learning algorithms can achieve a high sensitivity and specificity in pixel-level classification of amyloid and corneal stromal area. Further modeling and development of algorithms to assess earlier stages of deposition from clinical images is necessary to better assess the correlation between amyloid deposition and visual acuity. The method might be applied to corneal dystrophies as well.
Generic Attacks on Hash Combiners

Hash combiners are a practical way to make cryptographic hash functions more tolerant to future attacks and compatible with existing infrastructure. A combiner combines two or more hash functions in a way that is hopefully more secure than each of the underlying hash functions, or at least remains secure as long as one of them is secure. Two classical hash combiners are the exclusive-or (XOR) combiner $$ \mathcal {H}_1(M) \oplus \mathcal {H}_2(M) $$ and the concatenation combiner $$ \mathcal {H}_1(M) \Vert \mathcal {H}_2(M) $$. Both of them process the same message using the two underlying hash functions in parallel. Apart from parallel combiners, there are also cascade constructions sequentially calling the underlying hash functions to process the message repeatedly, such as Hash-Twice $$\mathcal {H}_2(\mathcal {H}_1(IV, M), M)$$ and the Zipper hash $$\mathcal {H}_2(\mathcal {H}_1(IV, M), \overleftarrow{M})$$, where $$\overleftarrow{M}$$ is the reverse of the message M. In this work, we study the security of these hash combiners by devising the best-known generic attacks. The results show that the security of most of the combiners is not as high as commonly believed. We summarize our attacks and their computational complexities (ignoring the polynomial factors) as follows: Our main technical contributions include the following:
Sensor-based Navigation of Omnidirectional Wheeled Robots Dealing with both Collisions and Occlusions

SUMMARY Navigation tasks are often subject to several constraints that can be related to the sensors (visibility) or come from the environment (obstacles). In this paper, we propose a framework for autonomous omnidirectional wheeled robots that takes into account both collision and occlusion risk, during sensor-based navigation. The task consists in driving the robot towards a visual target in the presence of static and moving obstacles. The target is acquired by fixed – limited field of view – on-board cameras, while the surrounding obstacles are detected by lidar scanners. To perform the task, the robot has not only to keep the target in view while avoiding the obstacles, but also to predict its location in the case of occlusion. The effectiveness of our approach is validated through several experiments.
Design of intelligent English writing self-evaluation auxiliary system

Since the reform and opening up, the exchanges between China and the world have become more and more frequent.English, as a widely used international language, plays an important role in international exchanges.English teaching includes five aspects, listening, speaking, reading, writing and translation.Writing teaching is very important but difficult.In order to improve students' autonomous writing ability, this paper briefly introduced the real-time multi-writing teaching mode and designed an automatic scoring algorithm of writing self-evaluation auxiliary system, random sampling based Bayesian classification and combinational algorithm.One thousand CET-4 and CET-6 compositions from Chinese Learner English Corpus (CLEC) were evaluated, and the scoring effect of Bayesian classification algorithm was also tested.The results showed that the accuracy rate, recall rate and F value of the proposed algorithm was better than that of Bayesian classification algorithm under 150 feature extraction dimensions, the two algorithms had improved scoring effect under the optimal feature extraction dimensions, and the improvement amplitude of the algorithm proposed in this study was larger.In summary, the random sampling based Bayesian classification and combinational algorithm is effective and feasible as an automatic scoring algorithm of writing self-evaluation auxiliary system.
Managing Performance Interference Effects for Intelligent and Efficient Virtual Machines Placement based on GWO Approach in Cloud

Cloud computing paradigm has been a trend in the computational world.Thus, many service providers today are competing to enhance their features to attract more customers as they are offering them a bunch of features through a pay-as-you-go pricing model.However, despite their huge fame, cloud environments still suffer from some issues that are being studied by researchers from various perspectives.One of the controversial cloud issues nowadays is interference among virtual machines (VMs) sharing the same hardware platform called also physical machine (PM).This problem occurs due to contention on shared resources (e.g.CPU, disk, memory, network I/O, etc.) between co-hosted VMs which results in a performance degradation.The co-hosting of VMs on the same PM, emerges from the ambition of server consolidation that cloud providers aim to reach in order to improve power efficiency and optimize resource utilization.Furthermore, the Virtual Machine Placement (VMP) is one of the most challenging problems in cloud environments management and it is being studied from various perspectives.Therefore, the key factor of successful server consolidation is to minimize performance interference among co-located VMs.In this paper, we are going to review two closely related research lines (i.e. the inter-VM interference detection and/or prediction and the interference-aware virtual machine placement in cloud computing environments), give a comparative study between the reviewed approaches for each of them and propose our Swarm intelligence-based metaheuristic, named Grey Wolf Optimizer (GWO) approach, for interference aware Virtual Machine Placement Problem (VMPP).
Direction, Velocity, Merging Probabilities and Shape Descriptors for Crowd Behavior Analysis

Descriptors are important for quantifying crowd behavior. The existing descriptors generally provide information about crowd density, i.e., number of people/objects present in a defined spatial area. However, other properties of crowd like speed, direction, shape, and merging probabilities (of different crowds at group level) are also important for crowd analysis. In this paper, crowd descriptors (by mitigating the effects of outliers) are introduced which can be used for crowds having various densities. The simulations on various datasets show the applicability of the proposed descriptors.
An Integrated Framework to Ensure Information Security Over the Internet

Information security is not a new concept in the technological industry.Information security is one of the major study areas in computer information systems.Due to the increasing popularity and dependency over internet, the need for proper information security has increased.There are new security paradigms arising every day to increase the information security.Determining the quality and value of the information, to set the proper system qualities to implement proper security is of high importance.However, the requirement of a more effective solution to be implemented in order to prevent the increasing security threats against the information on internet.Information security requires the integration of different perspectives including mathematical evaluation, technical evaluation, economic perspective and social perspective.Hence, in this paper, we are proposing an integrated framework to ensure information security over the internet.In the proposed framework, we incorporate mathematical and logical analysis techniques by incorporating methodological strategies and procedures along with different analysis techniques which will have different factors causing risk as the input parameters and generates one outputrisk, which is expressed as in terms of probability.Thus, the proposed framework for information security system stands above the existing systems in producing better results
STTAL DEVELOPMENT STRATEGY TO PRODUCE HUMAN RESOURCES THAT ACKNOWLEDGE TECHNOLOGY FACING THE INDUSTRIAL REVOLUTION ERA 4.0

The industrial revolution 4.0 requires that one must be able to work with increasingly sophisticated technological developments. The industrial revolution 4.0 emphasizes digital economic patterns, artificial intelligence, big data, robotic or known as the disruptive innovation phenomenon. The problem discussed in this study is how to determine the strategy for developing STTAL. The purpose of this study was to determine the best strategy for developing STTAL using the SWOT analysis method. The results of the study show that the development of STTAL to produce Human Resources that mastered the technology faced the era of industrial revolution 4.0. Namely: a). Increase the quantity and quality of laboratory facilities that are complete in stages according to the priority scale adjusting the available budget by means of regulation, cooperation, coordination and socialization to relevant stakeholders, b). Publication of lecturer and student research in national and international journals is one way to improve the ability and expertise to produce technological products of marine and maritime technology, c). Establish a curriculum for each Study Program based on IQF and lead to the science and technology fields of maritime and maritime technology, d). Establishing the ideal STTAL Organization will be able to support the operational education and development of new Cyber Study Programs. &#x0D; Keywords: SWOT Analysis, STTAL Development, Human Resources, Industrial Revolution Era 4.0.
Intelligent Human Resource Management Using Latent Semantic Analysis with the Internet of Things

The objective of this study is to design a conceptual framework of intelligent human resource management using latent semantic analysis (LSA) with the internet of things (IoT) by using Near Field Communication (NFC) technology and to design the intelligent human resource management architecture using latent semantic analysis with the Internet of Things using NFC.The aim is to develop a prototype in human resource management system development for application in an organization.The research results show that the conceptual framework consists of input in the form of HRM loT LSA.As far as the process is concerned, it was Intelligent Human Resource Management using Latent Semantic Analysis with the Internet of Things to operate in a High Performance Education Organization.The output with regard to the High Performance Education Organization had 4 components: 1) a mobile telephone that had NFC 2) NFC Tag used in imputing the data 3) processing the data by LSA and 4) an application showing the data.The system architecture was divided into 3 main parts: BYOD (Bring Your Own Device), HRM and an HPO Report.
Bi-Group Bayesian Personalized Ranking from Implicit Feedback

The personalized ranking is to provide a user with a ranked list of items, which is also called item recommendation. Item recommendation from implicit feedback has attracted much attention in recent years due to its broad application in real-world scenarios. In this work, we introduce the concept of Bi-Group: support group and opposition group. The members in the support group will support the user's positive feedback by rewarding item preference, and the members in the opposition group will object to the user's positive feedback by penalizing item preference. Based on Bi-Group, we present Bi-Group Bayesian Personalized Ranking(BiGBPR) algorithm that exploits these two groups of users to reward and penalize user's actions. Experiments on three real-world datasets show that BiGBPR can significantly improve the performance of item recommendation.
Character Identification Refined: A Proposal

Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.
The Fast and the Flexible: Training Neural Networks to Learn to Follow Instructions from Small Data

Learning to follow human instructions is a long-pursued goal in artificial intelligence.The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful of examples to learn from.Work in the past has relied on hand-coded components or manually engineered features to provide strong inductive biases that make learning in such situations possible.In contrast, here we seek to establish whether this knowledge can be acquired automatically by a neural network system through a two phase training procedure: A (slow) offline learning stage where the network learns about the general structure of the task and a (fast) online adaptation phase where the network learns the language of a new given speaker.Controlled experiments show that when the network is exposed to familiar instructions but containing novel words, the model adapts very efficiently to the new vocabulary.Moreover, even for human speakers whose language usage can depart significantly from our artificial training language, our network can still make use of its automatically acquired inductive bias to learn to follow instructions more effectively.
Task-Driven Common Representation Learning via Bridge Neural Network

This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task. The proposed approach employs two convolutional neural networks that project the two data sources into a feature space to learn the desired common representation required by the specific task. The training objective with artificial negative samples is introduced with the ability of mini-batch training and it’s asymptotically equivalent to maximizing the total correlation of the two data sources, which is verified by the theoretical analysis. The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.
You Write like You Eat: Stylistic Variation as a Predictor of Social Stratification

Inspired by Labov's seminal work on stylistic variation as a function of social stratification, we develop and compare neural models that predict a person's presumed socio-economic status, obtained through distant supervision, from their writing style on social media.The focus of our work is on identifying the most important stylistic parameters to predict socioeconomic group.In particular, we show the effectiveness of morpho-syntactic features as stylistic predictors of socio-economic group, in contrast to lexical features, which are good predictors of topic.
How Does Knowledge of the AUC Constrain the Set of Possible Ground-Truth Labelings?

Recent work on privacy-preserving machine learning has considered how datamining competitions such as Kaggle could potentially be “hacked”, either intentionally or inadvertently, by using information from an oracle that reports a classifier’s accuracy on the test set (Blum and Hardt 2015; Hardt and Ullman 2014; Zheng 2015; Whitehill 2016). For binary classification tasks in particular, one of the most common accuracy metrics is the Area Under the ROC Curve (AUC), and in this paper we explore the mathematical structure of how the AUC is computed from an n-vector of real-valued “guesses” with respect to the ground-truth labels. Under the assumption of perfect knowledge of the test set AUC c=p/q, we show how knowing c constrains the set W of possible ground-truth labelings, and we derive an algorithm both to compute the exact number of such labelings and to enumerate efficiently over them. We also provide empirical evidence that, surprisingly, the number of compatible labelings can actually decrease as n grows, until a test set-dependent threshold is reached. Finally, we show how W can be efficiently whittled down, through pairs of oracle queries, to infer all the groundtruth test labels with complete certainty.
The Limits of Lexicase Selection in an Evolutionary Robotics Task

Agents exhibiting generalized control are capable of solving a theme of related tasks, rather than a specific instance. Here, generalized control pertains to the locomotive capacity of quadrupedal ...
On a multilattice analogue of a hypersequent S5 calculus

In this paper, we present a logic MMLS5n which is a combination of multilattice logic and modal logic S5. MMLS5n is an extension of Kamide and Shramko’s modal multilattice logic which is a multilattice analogue of S4. We present a cut-free hypersequent calculus for MMLS5n in the spirit of Restall’s one for S5 and develop a Kripke semantics for MMLS5n, following Kamide and Shramko’s approach. Moreover, we prove theorems for embedding MMLS5n into S5 and vice versa. As a result, we obtain completeness, cut elimination, decidability, and interpolation theorems for MMLS5n. Besides, we show the duality principle for MMLS5n. Additionally, we introduce a modification of Kamide and Shramko’s sequent calculus for their multilattice version of S4 which (in contrast to Kamide and Shramko’s original one) proves the interdefinability of necessity and possibility operators. Last, but not least, we present Hilbert-style calculi for all the logics in question as well as for a larger class of modal multilattice logics.
Accelerating All-Edge Common Neighbor Counting on Three Processors

We propose to accelerate an important but time-consuming operation in online graph analytics, which is the counting of common neighbors for each pair of adjacent vertices (u,v), or edge (u,v), on three modern processors of different architectures. We study two representative algorithms for this problem: (1) a merge-based pivot-skip algorithm (MPS) that intersects the two sets of neighbor vertices of each edge (u,v) to obtain the count; and (2) a bitmap-based algorithm (BMP), which dynamically constructs a bitmap index on the neighbor set of each vertex u, and for each neighbor v of u, looks up v's neighbors in u's bitmap. We parallelize and optimize both algorithms on a multicore CPU, an Intel Xeon Phi Knights Landing processor (KNL), and an NVIDIA GPU. Our experiments show that (1) Both the CPU and the GPU favor BMP whereas MPS wins on the KNL; (2) Across all datasets, the best performer is either MPS on the KNL or BMP on the GPU; and (3) Our optimized algorithms can complete the operation within tens of seconds on billion-edge Twitter graphs, enabling online analytics.
Mixture of Expert/Imitator Networks: Scalable Semi-Supervised Learning Framework

The current success of deep neural networks (DNNs) in an increasingly broad range of tasks involving artificial intelligence strongly depends on the quality and quantity of labeled training data. In general, the scarcity of labeled data, which is often observed in many natural language processing tasks, is one of the most important issues to be addressed. Semisupervised learning (SSL) is a promising approach to overcoming this issue by incorporating a large amount of unlabeled data. In this paper, we propose a novel scalable method of SSL for text classification tasks. The unique property of our method, Mixture of Expert/Imitator Networks, is that imitator networks learn to “imitate” the estimated label distribution of the expert network over the unlabeled data, which potentially contributes a set of features for the classification. Our experiments demonstrate that the proposed method consistently improves the performance of several types of baseline DNNs. We also demonstrate that our method has the more data, better performance property with promising scalability to the amount of unlabeled data.
Interactive spatial augmented reality system for Chinese opera

In this research, the authors designed an interactive spatial augmented reality system for stage performance based on the technologies of UWB positioning and Bluetooth triggering. The position of the actor is obtained through the antenna tag carried by the actor and the signal base station placed on the stage. Special effects can be triggered through the Bluetooth module according to the actor. The system has a higher degree of freedom in practical applications, which can present an interactive spatial augmented reality effect, and therefore provide new possibilities for the application of spatial augmented reality in the stage performance. The system could bring better immersive experience to the audiences, and it also brings new possibilities for the aesthetic creation of opera.