Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Machine Learning to Energy Usage

With the vast increase in our access to data, there is a benefit to being able to analyze, model, and understand this data. The University of Minnesota Morris (UMM) has two wind turbines on campus that generate 60% of electricity for the campus in order to strive toward energy sustainability. Due to this desire for sustainability, there has been a significant increase in data gathering on wind turbine production and campus energy usage. UMM hopes to use this data to better understand energy usage and production trends. One way to try to better understand this data is to apply machine learning techniques, such as Neural Networks, decision trees, and Bayesian techniques, in order to create models that can help to explore relationships in the data. Machine learning entails creating models that can learn from past experiences to accurately classify or predict future situations. We have gathered large amounts of data related to energy production from various UMM campus sources. With this data, we plan to create models that are able to predict future wind turbine energy production using past production patterns and weather data. If the models are sufficiently accurate, these models could help the campus to understand energy usage and production trends. For example, members of the campus community could choose to schedule activities that consume large amounts of electricity on days when the wind turbines are producing large amounts of power. This could include small jobs like student laundry, or larger jobs related to campus heating. UMM would also be able to use the relationships found in the models to help better understand why the campus produces or uses large amounts of energy in hopes of improving energy production and conservation strategies.
Genome-Wide Association Studies of Anthracnose and Angular Leaf Spot Resistance in Common Bean (Phaseolus vulgaris L.)

The common bean (Phaseolus vulgaris L.) is the world’s most important legume for human consumption. Anthracnose (ANT; Colletotrichum lindemuthianum) and angular leaf spot (ALS; Pseudocercospora griseola) are complex diseases that cause major yield losses in common bean. Depending on the cultivar and environmental conditions, anthracnose and angular leaf spot infections can reduce crop yield drastically. This study aimed to estimate linkage disequilibrium levels and identify quantitative resistance loci (QRL) controlling resistance to both ANT and ALS diseases of 180 accessions of common bean using genome-wide association analysis. A randomized complete block design with four replicates was performed for the ANT and ALS experiments, with four plants per genotype in each replicate. Association mapping analyses were performed for ANT and ALS using a mixed linear model approach implemented in TASSEL. A total of 17 and 11 significant statistically associations involving SSRs were detected for ANT and ALS resistance loci, respectively. Using SNPs, 21 and 17 significant statistically associations were obtained for ANT and angular ALS, respectively, providing more associations with this marker. The SSR-IAC167 and PvM95 markers, both located on chromosome Pv03, and the SNP scaffold00021_89379, were associated with both diseases. The other markers were distributed across the entire common bean genome, with chromosomes Pv03 and Pv08 showing the greatest number of loci associated with ANT resistance. The chromosome Pv04 was the most saturated one, with six markers associated with ALS resistance. The telomeric region of this chromosome showed four markers located between approximately 2.5 Mb and 4.4 Mb. Our results demonstrate the great potential of genome-wide association studies to identify QRLs related to ANT and ALS in common bean. The results indicate a quantitative and complex inheritance pattern for both diseases in common bean. Our findings will contribute to more effective screening of elite germplasm to find resistance alleles for marker-assisted selection in breeding programs.
Knowledge learning based evolutionary algorithm for unconstrained optimization problem

In this paper, we propose a new evolutionary algorithm called nearest neighbor evolutionary algorithm (NNE) to solve the unconstrained optimization problem. Specifically, NNE consists of two major steps: coarse nearest neighbor evolutionary and fine nearest neighbor evolutionary. The coarse nearest neighbor evolutionary step pays more attention to searching the optimal solutions in the global way, while the fine nearest neighbor evolutionary step focuses on searching the best solutions in the local way. NNE repeats two major steps until the terminate condition is reached. NNE not only adopts the elitist strategy and maintains the best individuals for the next generation, but also considers the knowledge obtained in the searching process. The experiments demonstrate that (1) NNE achieves good performance in most of numerical optimization problems; (2) NNE outperforms most of state-of-art evolutionary algorithms, such as traditional genetic algorithm (GA), the jumping gene genetic algorithm (JGGA).
A New Approach to the Pareto Stable Matching Problem

In two-sided matching markets, the concept of stability proposed by Gale and Shapley is one of the most important solution concepts. In this paper, we consider a problem related to stability of a matching in a two-sided matching market with indifferences. It is known that stability does not guarantee Pareto efficiency in a two-sided matching market with indifferences. However, Erdil and Ergin proved that there always exists a stable and Pareto efficient matching in a many-to-one matching market with indifferences and gave a polynomial-time algorithm for finding it. Later on, Chen proved that there always exists a stable and Pareto efficient matching in a many-to-many matching market with indifferences and gave a polynomial-time algorithm for finding it. In this paper, we propose a new approach to the problem of finding a stable and Pareto efficient matching in a many-to-many matching market with indifferences. Our algorithm is an alternative proof of the existence of a stable and Pareto efficient matching in a many-to-many matching market with indifferences.
A Concept to Extend the Mobile Interaction Space with a Projected Aura

Miniaturization in projection technology makes it possible to integrate projectors into mobile devices like tablets, PDAs, or mobile phones. This offers new capabilities for input and output, which are seen as promising possibilities to overcome the limited screen size of mobile devices. However, it is still unclear, how projection will be used in combination with the traditional mobile device’s screen. In this paper, we propose a new concept, which allows users to extend the screen of their mobile device by using a set of integrated projectors. Taking advantage of the four directions, multiple projection spaces for interaction are created next to the mobile device’s screen, avoiding projection jitter, arm fatigue and attention shifts between projection and mobile device display, which are recurrent problems addressed in previous work.
Obstacle crossing with stereo vision for a quadruped robot

Autonomous locomotion in unstructured environments is a great challenge for quadruped robots, especially when there is little knowledge about the environments. Based on stereo vision, this paper proposes a locomotion control system for a quadruped robot that is able to perform adaptive locomotion in unknown environments cluttered with many obstacles. To reduce the negative influence of poor light, surface reflection, and textureless conditions, combined with the sum of absolute difference (SAD) method, an efficient post-processing algorithm is investigated to fulfill disparity estimation. According to the calculated terrain maps, a novel gait planning algorithm, which contains different gaits generation and gait transition, is designed to cross over terrains with enough stability and considerable locomotion speed. Experiments on the real robot of traversing complicated surface with stereo vision demonstrate the effectiveness of our system. Video footage can be seen at: http://irdc.ia. ac.cn/robot/.
Detection of cardiac autonomic neuropathy using linear parametric modeling of QT dynamics

In this study a linear parametric modeling technique is applied to model ventricular repolarisation (QT) variability from heart rate variability (RR) and respiration to investigate the change in cardiovascular system complexity in diabetes patients with cardiac autonomic neuropathy (CAN+) and without (CAN-). ECGs were recorded from 32 participants (20 CAN- and 12 CAN+), whilst in a supine resting position for 20 minutes and linear parametric autoregressive models using the first 250 beats were analysed. Models developed from the ECG derived respiration information could explain the QT-RR dynamics better than the models designed without any respiration information and may improve classification of CAN. QT-RR interaction provides useful information about cardiovascular system dynamics and autonomic nervous system modulation and ventricular repolarization on heart rate and heart rate variability.
LeBLEU: N-gram-based Translation Evaluation Score for Morphologically Complex Languages

This paper describes the LeBLEU evaluation score for machine translation, submitted to WMT15 Metrics Shared Task. LeBLEU extends the popular BLEU score to consider fuzzy matches between word n-grams. While there are several variants of BLEU that allow to non-exact matches between words either by character-based distance measures or morphological preprocessing, none of them use fuzzy comparison between longer chunks of text. The results on WMT data sets show that fuzzy n-gram matching improves correlations to human evaluation especially for highly compounding languages.
Many-threaded implementation of differential evolution for the CUDA platform

Differential evolution is an efficient populational meta -- heuristic optimization algorithm successful in solving difficult real world problems. Due to the simplicity of its operations and data structures, it is suitable for a parallel implementation on multicore systems and on the GPU. In this paper, we design a simple yet highly parallel implementation of the differential evolution using the CUDA architecture. We demonstrate the speedup obtained by the proposed parallelization of the differential evolution on an NP hard combinatorial optimization problem and on a benchmark function of many variables.
Soft-landing control by control invariance and receding horizon control

We propose a design for soft landing control based on control invariant sets and receding horizon control. Soft landing control, which is of interest in several applications in aerospace, transportation systems, and factory automation, aims at achieving precise positioning of a moving object to a target position, while ensuring that the maximum velocity decreases as the target is approached. The resulting soft contact avoids damages and wear. In this paper, we formulate appropriate constraints and recast soft landing control as the generation of an admissible trajectory of the constrained system. Then, we compute a control invariant set and design a receding horizon control law that forces the state to remain in such set. Thus, the trajectories generated by the controller achieve soft landing, regardless of the controller cost function and horizon, also when the dynamics are uncertain. We demonstrate our approach by a case study in transportation systems.
A Tabu-Based Cache to Improve Range Queries on Preﬁx Trees

—Distributed Hash Tables (DHTs) provide the substrate to build large scale distributed applications over Peer-to-Peer networks. A major limitation of DHTs is that they only support exact-match queries. In order to offer range queries over a DHT it is necessary to build additional indexing structures. Preﬁx-based indexes, such as Preﬁx Hash Tree (PHT), are interesting approaches for building distributed indexes on top of DHTs. Nevertheless, the lookup operation of these indexes usually generates a high amount of unnecessary trafﬁc overhead which degrades system performance by increasing response time. In this paper, we propose a novel distributed cache system called Tabu Preﬁx Table Cache (TPT-C), aiming at improving the performance of the Preﬁx-trees. We have implemented our solution over PHT, and the results conﬁrm that our searching approach reduces up to a 70% the search latency and trafﬁc overhead.
How to deal with amotivated students

Amotivation can be considered one of the more important problems faced in hospitality training. This research paper used commitment to reduce amotivation and increase performance in a training program. We used action identification to improve the effectiveness of commitment. Ninety-two students participated in the experiment. Broken down into three experimental conditions, 29 participated in a standard training program and 63 in one of two training programs using commitment (33 participants for the low identification commitment and 30 for the high). Results show that commitment increases performance and reduces amotivation. Furthermore, the link between participants’ commitment and performance appears to be mediated by amotivation. Finally, results are discussed and future avenues of research are suggested.
One-Time Key Authentication Protocol for PMIPv6

We are now going to the 4G network and in the 4G network environment, there are so many devices connected to the Internet while they move. We have protocol that can support movement of communicating node without any disruption of their connection status named Mobile IP (MIP). But, the major problem of this MIP is too heaviness of the protocol for small mobile nodes. So, IETF now propose Proxy MIP to solve this problem. But, there is no way to authenticate the mobile node in PMIP. In this paper, we propose new authentication protocol for PMIPv6 and show the results of analysis. With our proposed protocol, we can give a lot of securing features to current PMIPv6.
The Impact of Animated Karaoke on Vocabulary Acquisition Among Japanese Language Learners in Malaysian Universities

This research paper aimed to explore and investigate whether the use of animated karaoke in the Japanese language classes has an influence on vocabulary acquisition among Malaysian Japanese language learners. The study was conducted on 100 students learning the Japanese language at the beginner level as an elective subject at the Faculty of Modern Languages and Communication, Universiti Putra Malaysia (UPM). Data were collected through questionnaires, interviews, and observations. The analytical descriptive result showed that the participants were able to memorize and understand Japanese vocabulary quickly and easily after watching animation consisting of visual graphics combined with text which stimulate their brain. Data from the interviews also showed that the target vocabulary items were pronounced accurately and correctly when learners sang along while watching the animated karaoke. The triangulation of data showed that animated karaoke is a good tool to engage participants in learning the Japanese language while at the same time, having fun and feeling highly motivated to explore the language. The implication of the study suggests that the use of animated karaoke in other foreign language classes will help boost students’ confidence and motivation level.
INFORMATION SECURITY ATTACK TREE MODELING An Effective Approach for Enhancing Student Learning

This paper presents a framework for enhancing student learning about the vulnerabilities of information assets of a business enterprise using attack tree modeling. Using this framework, students get an overview of the methodology as well as learn how to implement it with a well-known list of information security vulnerabilities. As a result, students can provide input into threat modeling strategies and operating procedures and thus, increase overall confidentiality, integrity, and availability of computer and network systems. The paper concludes by describing a corresponding system capability metric that a system administrator, student, or red team can use to test the vulnerabilities of the systems within a business enterprise.
Practice and Pitfalls in NMO-based Differential Semblance Velocity Analysis

Differential semblance provides an automated alternative to commonly used velocity analysis methods. For data exhibiting low structural relief, an implementation based on hyperbolic moveout is fast enough for 3D velocity estimation on a workstation. Application of this technique to a time processed land 3D dataset from the onshore Gulf of Mexico, demonstrates this capability. Compared to more convention methods of velocity analysis, it produced reliable results in very short order.
A bound for Dickson's lemma

We consider a special case of Dickson's lemma: for any two functions $f,g$ on the natural numbers there are two numbers $i<j$ such that both $f$ and $g$ weakly increase on them, i.e., $f_i\le f_j$ and $g_i \le g_j$. By a combinatorial argument (due to the first author) a simple bound for such $i,j$ is constructed. The combinatorics is based on the finite pigeon hole principle and results in a descent lemma. From the descent lemma one can prove Dickson's lemma, then guess what the bound might be, and verify it by an appropriate proof. We also extract (via realizability) a bound from (a formalization of) our proof of the descent lemma. Keywords: Dickson's lemma, finite pigeon hole principle, program extraction from proofs, non-computational quantifiers.
Countering median filtering anti-forensics and performance evaluation of forensics against intentional attacks

Median filtering forensics and its anti-forensic attack have received considerable attention since median filtering can be used for both image enhancement and anti-forensic purposes. A median filtering anti-forensic attack method by adding uniformly distributed noise was proposed in an image pixel domain. However, we observe that this attack method leaves visible traces in the histogram of its median filtering residual (MFR) and can be detected using a histogram bin ratio of its MFR in the textured area. In order to eliminate this trace left in the MFR, we propose to adding noise adaptively in pixel domain to keep a constant minimal SNR. The performance of several forensic methods are evaluated under several attacks, it shows that the AR (autoregressive) forensic method has the most robustness against intentional attacks compared with the other forensic methods.
Robust compression and transmission of MPEG-4 video

This paper discusses issues related to the delivery of MPEG-4 video over the Internet and wireless channels. MPEG-4's built-in error resilience capabilities such as flexible re-synchronization markers, data partitioning, header protection, reversible VLCs, and forced intra-frame refresh are described. Methods for using these techniques to build a “smart” network decoder are discussed, and the decoder's video quality is measured for various channel error conditions. The effectiveness and overheads of the various error resilience techniques are compared using both peak signal-to-noise ratio measurements and expert viewing. The use of forward error correcting strategies and the effects of packet sizes and boundaries on video quality are also examined.
Normal distribution transform graph-based point cloud segmentation

We present a graph-based algorithm for segmenting point cloud scenes using criteria based on the combination of spatial, geometric, and appearance features. An octree data structure is employed to organize the point cloud data. The voxel space is used to create a Normal Distribution Transform Feature Representation (NDT-FR) to model the underlying sensor data and corresponding features in a probabilistic manner. The proposed segmentation algorithm uses the Hellinger distance calculated on local statistics stored in neighboring voxels to define the edge weights of the graph. Rather than choosing a specific feature for edge weight calculation, our approach has the ability to combine multiple features into a single edge weight without the need to find an appropriate normalization scheme. We verify our algorithm on multiple indoor scenes and perform a qualitative evaluation. We also show how our edge weighting scheme can increase the accuracy of object boundaries in the final segmentation.
Textual visualization based on readability

Many previous examples of text visualization have generally focused on a book's contents and on plot analysis, but these approaches hardly provide practical clues needed for a reader to pick the right book for them, out of the thousands of recommendations on an Internet book store's Web site. Common readers' questions are not academic but practical: How much time will I need to read this book? Is it easy enough for a third-grade kid? This paper suggests that intuitive text visualization reflecting readability to support reader book selection.
Nicotinic filtering of sensory processing in auditory cortex

Although it has been known for decades that the drug nicotine can improve cognitive function, the nature of its effects and the underlying mechanisms are not well understood. Nicotine activates nicotinic acetylcholine (ACh) receptors (nAChRs) that normally are activated by endogenous ACh, presumably “hijacking” the cholinergic contribution to multiple cognitive functions, notably attention. Thus, studying nicotine's effects helps to better understand a commonly used drug as well as functions of nAChRs. Moreover, nicotinic agonists are being developed to treat a variety of disorders that involve attention-related or age-related cognitive dysfunction. Studies have shown that nicotine can enhance processing of attended stimuli and/or reduce processing of distracters; that is, nicotine enhances attentional filtering. To examine potential mechanisms within sensory cortex that may contribute to cognitive functions, here we describe nicotinic actions in primary auditory cortex, where well-characterized neural “filters”—frequency receptive fields—can be exploited to examine nicotinic regulation of cortical processing. Using tone-evoked current-source density (CSD) profiles, we show that nicotine produces complex, layer-dependent effects on spectral and temporal processing that, broadly speaking, enhance responses to characteristic frequency (optimal) stimuli while simultaneously suppressing responses to spectrally distant stimuli. That is, nicotine appears to narrow receptive fields and enhances processing within the narrowed receptive field. Since basic cortical circuitry and nAChR distributions are similar across neocortex, these findings may generalize to neural processing in other sensory regions, and to non-sensory regions where afferent inputs are more difficult to manipulate experimentally. Similar effects across sensory and non-sensory cortical circuits could contribute to nicotinic enhancement of cognitive functions.
Multiagent Collaborative Task Learning through Imitation

Learning through imitation is a powerful approach for acquiring new behaviors. Imitation-based methods have been successfully applied to a wide range of single agent problems, consistently demonstrating faster learning rates compared to exploration-based approaches such as reinforcement learning. The potential for rapid behavior acquisition from human demonstration makes imitation a promising approach for learning in multiagent systems. In this work, we present results from our single agent demonstration-based learning algorithm, aimed at reducing demonstration demand of a single agent on the teacher over time. We then demonstrate how this approach can be applied to effectively train a complex multiagent task requiring explicit coordination between agents. We believe that this is the first application of demonstration-based learning to simultaneously training distinct policies to multiple agents. We validate our approach with experiments in two complex simulated domains.