Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the Impact of Workplace Literacy Programs. Results and Instruments from the NCAL Workplace Literacy Impact Project.

This two-part document reports the results of a literature review of workplace literacy program evaluation and then details a study of two current workplace literacy programs. The five chapters of the first part summarize findings on what is known about workplace literacy programs, methods for evaluating workplace literacy programs, assessing workplace literacy program results, assessing the impact on family literacy, and assessing the impact on productivity. The second part describes the study, the purposes of which were to develop an impact assessment model for workplace literacy programs and to produce data on the impact of programs at two sites, as well as to refine the model for use at other sites. The two sites chosen were very different but both operate established programs involving technical and communications training, high school equivalency programs, and English-as-a-Second-Language classes. Preand post-program data were gathered on learners' job productivity, literacy attributes, and literacy practices with their families through interviews, tests, and questionnaires, and supervisor ratings. Results showed that learners made gains in their literacy self-image, their ability to articulate plans, the amount and range of literacy activity both at work and away from work, and their reading strategies and comprehension. The study concluded that the evaluation process developed can be used as a model for evaluation of workplace evaluation projects. Eight appendices contain project forms, and a bibliography lists 79 references. (KC) EVALUATING THE IMPACT OF WORKPLACE LITERACY PROGRAMS RESULTS AND INSTRUMENTS FROM THE NCAL WORKPLACE LT TRACY IMPACT PROJECT Larry Mikulecky and Paul Lloyd SCHOOL OF EDUCATION INDIANA UNIVERSITY BLOOMINGTON U.S. DEPARTMENT Of EDUCATION Office of Ed hone! Research and improvement EOUCA NAL RESOURCES INFORMATION CENTER (ERIC) his document has been reproduced as received from the person or organization Originating it 0 Minor changes have been made to improve reproduction quality Points of view or opinions stated in this document do not necessarily represent official OE RI position or policy
Modified decorrelating decision-feedback detection of BLAST space-time system

We propose a stable and reduced-complexity detection method for the Bell Labs layered space-time (BLAST) coding system. The existing iterative nulling and cancellation algorithm for BLAST has high computational complexity and requires repeated matrix pseudo-inverse calculation which may lead to numerical instability. A square-root algorithm was proposed by other researchers to reduce complexity and improve numerical stability. In this paper, to further reduce complexity, we modify the decorrelating decision-feedback CDMA multiuser detection method and apply it to BLAST. Similar to the square-root algorithm, numerical stable unitary transformations are performed on the Cholesky-decomposed matrices to reorder the detection and cancellation steps. However, our method exploits a symmetry property in the triangularization process, which may further improve the numerical stability and reduce computational complexity over that of the square-root algorithm. In the simulation results, the impact of the reordering on performance is demonstrated.
Comparative study of DL-selenomethionine vs sodium selenite and seleno-yeast on antioxidant activity and selenium status in laying hens.

The aim of this study was to compare the effect of DL-selenomethionine (SM) with 2 routinely used Se sources, sodium selenite (SS) and seleno-yeast (SY), on relative bioavailability based on antioxidant activity and tissue Se content. Six hundred thirty 131-day-old brown laying hens were randomly assigned to 7 treatments for 168 d (24 wks) with 6 replicates of 15 hens per replicate. The SS and SY animals were supplemented a cornmeal and soybean diet that supplied a total Se 0.3 mg/kg whereas SM was added at 4 different levels to the total Se at 0.1, 0.3, 0.5 and 0.7 mg/kg. All hens fed the Se-supplemented diet showed higher glutathione peroxidase (GSH-Px) activity (P < 0.01), higher superoxide dismutase (SOD) activity (P < 0.05), lower malondialdehyde (MDA) content (P < 0.05) in plasma, and greater Se contents in egg yolks, albumen, leg muscle, breast muscle, liver, and plasma compared with those fed the control diet (P < 0.01). The organic sources (SY and SM) exhibited a greater ability to increase the GSH-Px activity (P < 0.01) and Se content in albumen (P < 0.01), leg, and breast muscles (P = 0.0099 and P = 0.0014, respectively) than the SS that was added at 0.3 mg Se/kg. The higher SM added levels increased the GSH-Px activity until the dose of 0.5mg Se/kg (P < 0.01). The greater Se concentrations in albumen, muscle and liver appeared in the higher SM-added level, as well as above the dose of 0.1 mg Se/kg (P < 0.01). In addition, hens fed the diet with SM accumulated more Se in albumen, leg, and breast muscle than those fed diets with SY (P < 0.05). These results confirmed the higher ability of organic Se sources to increase the antioxidant activity and Se deposition in egg albumen, leg, and breast muscles compared with SS, and demonstrated a significantly better efficiency of SM compared with SY for albumen and muscle Se enrichment.
EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach

Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for driver's safety. Typically, physiological studies on real-time detection of drowsiness usually use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects, group statistics may not be useful to accurately predict changes in cognitive states. Researchers have attempted to build subject-dependent models based on his/her pilot data to account for individual variability. Such approaches cannot account for the cross-session variability in EEG dynamics, which may cause problems due to various reasons including electrode displacements, environmental noises, and skin-electrode impedance. Hence, we propose an unsupervised subject- and session-independent approach for detection departure from alertness in this study. Experimental results showed that the EEG power in the alpha-band (as well as in the theta-band) is highly correlated with changes in the subject's cognitive state with respect to drowsiness as reflected through his driving performance. This approach being an unsupervised and session-independent one could be used to develop a useful system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
Estimation of Word Representations Using Recurrent Neural Networks and Its Application in Generating Business Fingerprints

Word vectors have been used as a useful feature in many NLP related tasks. In this project, we proposed a modified recurrent neural network algorithm to learn word representations from corpus in a state-transition manner. Our state-transition RNN could reach the results of state-of-the-art word embedding algorithms in terms of comparing Euclidean distance. With smaller hidden size, the state-transition RNN needs shorter time for training. As a testbench for our model, we trained our model on Yelp review corpus and implemented three different applications (business similarity, business search and business recommendation). Experimental results revealed that our state-transition RNN and our Business fingerprints generation algorithms achieved expected results.
The Restoration of Passive Rotational Tibio-Femoral Laxity after Anterior Cruciate Ligament Reconstruction

While the anterior cruciate ligament (ACL) is considered one of the most important ligaments for providing knee joint stability, its influence on rotational laxity is not fully understood and its role in resisting rotation at different flexion angles in vivo remains unknown. In this prospective study, we investigated the relationship between in vivo passive axial rotational laxity and knee flexion angle, as well as how they were altered with ACL injury and reconstruction. A rotometer device was developed to assess knee joint rotational laxity under controlled passive testing. An axial torque of ±2.5Nm was applied to the knee while synchronised fluoroscopic images of the tibia and femur allowed axial rotation of the bones to be accurately determined. Passive rotational laxity tests were completed in 9 patients with an untreated ACL injury and compared to measurements at 3 and 12 months after anatomical single bundle ACL reconstruction, as well as to the contralateral controls. Significant differences in rotational laxity were found between the injured and the healthy contralateral knees with internal rotation values of 8.7°±4.0° and 3.7°±1.4° (p = 0.003) at 30° of flexion and 9.3°±2.6° and 4.0°±2.0° (p = 0.001) at 90° respectively. After 3 months, the rotational laxity remained similar to the injured condition, and significantly different to the healthy knees. However, after 12 months, a considerable reduction of rotational laxity was observed towards the levels of the contralateral controls. The significantly greater laxity observed at both knee flexion angles after 3 months (but not at 12 months), suggests an initial lack of post-operative rotational stability, possibly due to reduced mechanical properties or fixation stability of the graft tissue. After 12 months, reduced levels of rotational laxity compared with the injured and 3 month conditions, both internally and externally, suggests progressive rotational stability of the reconstruction with time.
Sanctum: Minimal RISC Extensions for Isolated Execution

Sanctum is a set of minimal extensions to a standard RISC architecture that offers strong provable isolation of software modules running concurrently and sharing resources. Sanctum is similar to SGX in its API, but protects against an important class of additional software attacks, including cache timing and memory access pattern attacks. It does so via a principled approach to eliminating entire attack surfaces through isolation rather than plugging attack-specific privacy leaks. Sanctum’s hardware changes over a standard RISC architecture do not impact the cycle time, as they do not extend critical execution paths. Sanctum does not change any major CPU building block (e.g., ALU, MMU, cache), and only requires additional hardware at the interfaces between these building blocks corresponding to less than two percent chip area overhead. Over a set of benchmarks, Sanctum’s worst observed overhead for isolated execution is 14.6% over an idealized insecure baseline.
Learning Parallel Grammar Systems for a Human Activity Language

We have empirically discovered that the space of human actions has a linguistic structure. This is a sensory-motor space consisting of the evolution of the joint angles of the human body in movement. The space of human activity has its own phonemes, morphemes, and sentences. In kinetology, the phonology of human movement, we define atomic segments (kinetemes) that are used to compose human activity. In this paper, we present a morphological representation that explicitly contains the subset of actuators responsible for the activity, the synchronization rules modeling coordination among these actuators, and the motion pattern performed by each participating actuator. We model a human action with a novel formal grammar system, named Parallel Synchronous Grammar System (PSGS), adapted from Parallel Communicating Grammar Systems (PCGS). We propose a heuristic PArallel Learning (PAL) algorithm for the automatic inference of a PSGS. Our algorithm is used in the learning of human activity. Instead of a sequence of sentences, the input is a single string for each actuator in the body. The algorithm infers the components of the grammar system as a subset of actuators, a CFG grammar for the language of each component, and synchronization rules. Our framework is evaluated with synthetic data and real motion data from a large scale motion capture database containing around 200 different actions corresponding to verbs associated with voluntary observable movement. On synthetic data, our algorithm achieves 100% success rate with a noise level up to 7%.
Alzheimer’s Disease, Diagnosis and the Need for Biomarkers

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of aging that presents with memory loss, disorientation, confusion and a reduction in cognitive ability. Although a definite diagnosis of the disorder can only be made post-mortem by histopathological analysis, a number of methods are currently available for the in vivo assessment of AD including psycho-metric tests and neuro-imaging. However, these clinical assessments are relatively nonspecific and imaging is very costly. Genetic testing can be performed if familial AD is suspected, although such cases represent a very small minority of total AD cases. Apolipoprotein E genotype provides a measure for analysing the risk of developing AD, but does not act as an absolute predictive biomarker for AD. Therefore there is a need for an accurate, universal, specific and cost-effective biomarker to facilitate not only ante-mortem diagnosis of AD, but also to allow progression of the disease and response to therapy to be monitored. This is the ultimate goal that our group is pursuing through the pan-European AddNeuroMed project.
Chronic Kidney Disease Progression and Cardiovascular Outcomes Following Cardiac Catheterization—A Population‐Controlled Study

Background Studies of kidney disease associated with cardiac catheterization typically rely on billing records rather than laboratory data. We examined the associations between percutaneous coronary interventions, acute kidney injury, and chronic kidney disease progression using comprehensive Veterans Affairs clinical and laboratory databases. Methods and Results Patients undergoing percutaneous coronary interventions between 2005 and 2010 (N=24 405) were identified in the Veterans Affairs Clinical Assessment, Reporting, and Tracking registry and examined for associated acute kidney injury and chronic kidney disease development or progression relative to 24 405 matched population controls. Secondary outcomes analyzed included dialysis, acute myocardial infarction, and mortality. The incidence of chronic kidney disease progression following percutaneous coronary interventions complicated by acute kidney injury, following uncomplicated coronary interventions, and in matched controls were 28.66, 11.15, and 6.81 per 100 person‐years, respectively. Percutaneous coronary intervention also increased the likelihood of chronic kidney disease progression in both the presence and absence of acute injury relative to controls in adjusted analyses (hazard ratio [HR], 5.02 [95% CI, 4.68–5.39]; and HR, 1.76 [95% CI, 1.70–1.86]). Among patients with estimated glomerular filtration rate <60 mL/min per 1.73 m2, acute kidney injury increased the likelihood of disease progression by 8‐fold. Similar results were observed for all secondary outcomes. Conclusions Acute kidney injury following percutaneous coronary intervention was associated with increased chronic kidney disease development and progression and mortality.
Normalization of non-standard words

In addition to ordinary words and names, real text contains non-standard “words" (NSWs), including numbers, abbreviations, dates, currency amounts and acronyms. Typically, one cannot find NSWs in a dictionary, nor can one find their pronunciation by an application of ordinary “letter-to-sound" rules. Non-standard words also have a greater propensity than ordinary words to be ambiguous with respect to their interpretation or pronunciation. In many applications, it is desirable to “normalize" text by replacing the NSWs with the contextually appropriate ordinary word or sequence of words. Typical technology for text normalization involves sets of ad hoc rules tuned to handle one or two genres of text (often newspaper-style text) with the expected result that the techniques do not usually generalize well to new domains. The purpose of the work reported here is to take some initial steps towards addressing deficiencies in previous approaches to text normalization. We developed a taxonomy of NSWs on the basis of four rather distinct text types? news text, a recipes newsgroup, a hardware-product-specific newsgroup, and real-estate classified ads. We then investigated the application of several general techniques including n-gram language models, decision trees and weighted finite-state transducers to the range of NSW types, and demonstrated that a systematic treatment can lead to better results than have been obtained by the ad hoc treatments that have typically been used in the past. For abbreviation expansion in particular, we investigated both supervised and unsupervised approaches. We report results in terms of word-error rate, which is standard in speech recognition evaluations, but which has only occasionally been used as an overall measure in evaluating text normalization systems.
Extraction error modeling and automated model debugging in high-performance low power custom designs

Test model generation is common in the design cycle of custom made high performance low power designs targeted for high volume production. Logic extraction is a key step in test model generation to produce a logic level netlist from the transistor level representation. This is a semi-automated process which is error prone. The paper analyzes typical extraction errors applicable to clocking schemes seen in high-performance designs today. An automated debugging solution for these errors in designs with no state equivalence information is also presented. A suite of experiments on circuits with similar architectures to those found in the industry confirm the fitness and practicality of the solution.
Using blind analysis for software engineering experiments

Context: In recent years there has been growing concern about conflicting experimental results in empirical software engineering. This has been paralleled by awareness of how bias can impact research results. Objective: To explore the practicalities of blind analysis of experimental results to reduce bias. Method: We apply blind analysis to a real software engineering experiment that compares three feature weighting approaches with a naïve benchmark (sample mean) to the Finnish software effort data set. We use this experiment as an example to explore blind analysis as a method to reduce researcher bias. Results: Our experience shows that blinding can be a relatively straightforward procedure. We also highlight various statistical analysis decisions which ought not be guided by the hunt for statistical significance and show that results can be inverted merely through a seemingly inconsequential statistical nicety (i.e., the degree of trimming). Conclusion: Whilst there are minor challenges and some limits to the degree of blinding possible, blind analysis is a very practical and easy to implement method that supports more objective analysis of experimental results. Therefore we argue that blind analysis should be the norm for analysing software engineering experiments.
Nucleosomes Are Stably Evicted from Enhancers but Not Promoters upon Induction of Certain Pro-Inflammatory Genes in Mouse Macrophages

Chromatin is thought to act as a barrier for binding of cis-regulatory transcription factors (TFs) to their sites on DNA and recruitment of the transcriptional machinery. Here we have analyzed changes in nucleosome occupancy at the enhancers as well as at the promoters of three pro-inflammatory genes when they are induced by bacterial lipopolysaccharides (LPS) in primary mouse macrophages. We find that nucleosomes are removed from the distal enhancers of IL12B and IL1A, as well as from the distal and proximal enhancers of IFNB1, and that clearance of enhancers correlates with binding of various cis-regulatory TFs. We further show that for IFNB1 the degree of nucleosome removal correlates well with the level of induction of the gene under different conditions. Surprisingly, we find that nucleosome occupancy at the promoters of IL12B and IL1A does not change significantly when the genes are induced, and that a considerably fraction of the cells is occupied by nucleosomes at any given time. We hypothesize that competing nucleosomes at the promoters of IL12B and IL1A may play a role in limiting the size of transcriptional bursts in individual cells, which may be important for controlling cytokine production in a population of immune cells.
A semantic-based transaction model for active heterogeneous database systems

Presents a framework to process transactions in active heterogeneous database systems. In order to schedule the transactions correctly with high performance, the framework provides semantic-based concurrency control on the global level. It relaxes the correct criterion (global serializability) to allow the global sub-transactions on each site to execute in a different order. The global transactions include data transactions and rule transactions. A data transaction is a general database transaction. A rule transaction is formed by active rules during the data transaction. During the global transaction process, both data and rule transactions are managed by different control modules. The data transaction is decomposed into several global sub-transactions. The global semantic-based transaction module produces a correct parallel schedule for the global sub-transactions. The rule transaction may generate new global transactions during rule inference. The advantages of providing semantic-based transactions on the global level are that they reduce the number of aborts produced by the incorrect global ordering of the local database schedules and they improve the performance of the global transactions.
Fault-tolerant Localization for multi-UAV cooperative flight

A novel fault-tolerant localization method for low-cost UAVs flying at constant altitude is proposed in this paper, which is based on measuring relative ranges from one UAV to other UAVs. Contrast to the traditional navigation methods of single aerial vehicle, like INS/GPS and SINS/GPS, the proposed method is more suitable for low-cost small-size UAVs because of its low requirement to the navigation device. Furthermore, its localization accuracy is higher than other methods for multi-UAV because the sharable information in multi-UAV network is made full of use. Similar to the principle of GPS, the method takes three other UAVs as the reference points of an UAV whose GPS receiver works improperly due to failure. Thus the UAV's location in 2D horizontal plane can be determined by using the relative ranges from the faulty UAV to the other three UAVs at known location in inertial coordinate system. In order to improve the accuracy of estimated location, a Kalman filter is designed, which can calculate the variance of observations in terms of horizontal dilution of positioning (HDOP) adaptively. Meanwhile, option of the reference points is also optimized in the paper. Simulation results in Matlab\Simulink show the effectiveness of the proposed approach.