Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative study of linear learning methods in click-through rate prediction

A major challenge in the current era of search engine advertising is choosing which advertisements to show in response to a user query. This significantly impacts the overall user experience, and more importantly the advertising revenue stream for the search engine provider. Predicting click-through rates (CTR) for an advertisement is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. This study examines the performance of some well-known statistical learning methods (linear and logistic) with respect to their efficiency in predicting the click through rate of an impression, where an impression can simply be defined as an instance of a particular advertisement, with each instance defined in terms of the learning parameters in our data set. Our data set consisted of three types of independent attributes to act as a regressor in predicting our dependent variable - the app through which it was clicked, the site type and the domain to which it led - with the help of other anonymised variables. Fine tuning of the algorithm parameters was done to get promising results. Besides that a dimensionality check on the data set was conducted to observe the possibilities of dimensionality reduction. Logistic loss (log-loss) was used as the validation index in all cases. Our observations led us to the conclusion that with minimal data preprocessing, linear models give competitive on-par results suited for most practical applications, where the learning method chosen should not be computationally expensive. We go on to further verify this claim by comparing the performance of linear models on various subsets of the data set attributes, showing that the performance of the linear techniques was consistent all across.
Zn Subcellular Distribution in Liver of Goldfish (Carassius Auratus) with Exposure to Zinc Oxide Nanoparticles and Mechanism of Hepatic Detoxification

Zinc Oxide Nanoparticles (ZnO NPs) have attracted increasing concerns because of their widespread use and toxic potential. In this study, Zn accumulations in different tissues (gills, liver, muscle, and gut) of goldfish (Carassius auratus) after exposure to ZnO NPs were studied in comparison with bulk ZnO and Zn2+. And the technique of subcellular partitioning was firstly used on the liver of goldfish to study the hepatic accumulation of ZnO NPs. The results showed that at sublethal Zn concentration (2 mg/L), bioaccumulation in goldfish was tissue-specific and dependent on the exposure materials. Compared with Zn2+, the particles of bulk ZnO and the ZnO NPs appeared to aggregate in the environmentally contacted tissues (gills and gut), rather than transport to the internal tissues (liver and muscle). The subcellular distributions of liver differed for the three exposure treatments. After ZnO NPs exposure, Zn percentage in metal-rich granule (MRG) increased significantly, and after Zn2+ exposure, it increased significantly in the organelles. Metallothionein-like proteins (MTLP) were the main target for Zn2+, while MRG played dominant role for ZnO NPs. The different results of subcellular distributions revealed that metal detoxification mechanisms of liver for ZnO NPs, bulk ZnO, and Zn2+ were different. Overall, subcellular partitioning provided an interesting start to better understanding of the toxicity of nano- and conventional materials.
The Multivalued and Continuous Perceptrons

Rosenblatt's perceptron is extended to (1) a multivalued perceptron and (2) to a continuous-valued perceptron. It shown that any function that can be represented by the multivalued perceptron can be learned in a nite number of steps, and any function that can represented by the continuous perceptron can be learned with arbitrary accuracy in a nite number of steps. The whole apparatus is deened in the complex domain. With these perceptrons learnability is extended to more complicated functions than the usual linearly separable ones. The complex domain promises to be a fertile ground for neural networks research. One of the most remarkable and important theorems in the neural networks literature is Rosenblatt's Perceptron Theorem Ros62]. Simply stated, it says that if there is a weight vector W such that W T X > 0 whenever pattern vector X belongs to class ! 1 and W T X < 0 whenever X belongs to class ! 0 , i.e. the two classes are linearly separable, then a given simple learning algorithm will nd a weight vector W that will satisfy the above two inequalities. The superscript T indicates matrix transpose. In other words, if the pattern vectors, which belong to two classes ! 0 and ! 1 , are linearly separable, then the algorithm will nd a hyperplane that separates the two classes. The activation function used is the hard thresholding function, which gives two outputs: ? 1 or 1. In this work Rosenblatt's Perceptron Theorem is generalized to (1) the Multivalued Perceptron, where the output can take more than two discrete values, and (2) the Continuous Perceptron, where the output takes continuous values. In both cases all quantities involved are complex-valued. The output of the activation functions falls on the unit circle in the complex plane. Rosenblatt's perceptron is a special case of both perceptrons. The activation functions were previously used in Noe88a] and Noe88b] in a diierent context, and are diierent from the one used for the complex backpropagation algorithm in Geo92b, GK92].
Power/Performance Trade-Offs in Real-Time SDRAM Command Scheduling

Real-time safety-critical systems should provide hard bounds on an applications' performance. SDRAM controllers used in this domain should therefore have a bounded worst-case bandwidth, response time, and power consumption. Existing works on real-time SDRAM controllers only consider a narrow range of memory devices, and do not evaluate how their schedulers' performance varies across memory generations, nor how the scheduling algorithm influences power usage. The extent to which the number of banks used in parallel to serve a request impacts performance is also unexplored, and hence there are gaps in the tool set of a memory subsystem designer, in terms of both performance analysis, and configuration options. This article introduces a generalized close-page memory command scheduling algorithm that uses a variable number of banks in parallel to serve a request. To reduce the schedule length for DDR4 memories, we exploit bank grouping through a pairwise bank-group interleaving scheme. The algorithm is evaluated using an ILP formulation, and provides schedules of optimal length for most of the considered LPDDR, DDR2, DDR3, LPDDR2, LPDDR3 and DDR4 devices. We derive the worst-case bandwidth, power and execution time for the same set of devices, and discuss the observed trade-offs and trends in the scheduler-configuration design space based on these metrics, across memory generations.
Higher risk of offspring schizophrenia following antenatal maternal exposure to severe adverse life events.

CONTEXT Most societies believe that a mother's psychological state can influence her unborn baby. Severe adverse life events during pregnancy have been consistently associated with an elevated risk of low birth weight and prematurity. Such events during the first trimester have also been associated with risk of congenital malformations. OBJECTIVE To assess the effect in offspring of antenatal maternal exposure to an objective measure of stress on risk of adverse neurodevelopment, specifically schizophrenia. We hypothesized that the strongest relationship would be to maternal exposures during the first trimester. DESIGN Population-based study. SETTING Denmark. PARTICIPANTS In a cohort of 1.38 million Danish births from 1973 to 1995, mothers were considered exposed if 1 (or more) of their close relatives died or was diagnosed with cancer, acute myocardial infarction, or stroke syndrome up to 6 months before conception or during pregnancy. Offspring were followed up from their 10th birthday until their death, migration, onset of schizophrenia, or June 30, 2005; admissions were identified by linkage to the Central Psychiatric Register. Main Outcome Measure Schizophrenia. RESULTS The risk of schizophrenia and related disorders was raised in offspring whose mothers were exposed to death of a relative during the first trimester (adjusted relative risk, 1.67 [95% confidence interval, 1.02-2.73]). Death of a relative during other trimesters or up to 6 months before pregnancy were not linked with a higher risk of schizophrenia. CONCLUSIONS Our population-based study suggests that severe stress to a mother during the first trimester may alter the risk of schizophrenia in offspring. This finding is consistent with ecological evidence from whole populations exposed to severe stressors and suggests that environment may influence neurodevelopment at the feto-placental-maternal interface.
Efficient and Secure Handover Scheme for VANETS

A Vehicular Ad-Hoc Network or VANET is a technology were the moving cars act as terminals so that it can create a mobile network. In VANET every car which is participating in a network turns into a wireless router or node. The range between the communicating vehicles is about approximately 100 to 300 meters. In WAVE (Wireless Access in vehicular Environments) the terminal devices are moving with the much higher speeds, due to this many of the charectertics will be hampered (eg: QOS, Streaming etc). The Hampering of this charectertics may result in the increase in the latency. The traffic in VANETS goes on varying. Therefore there is an every chance that these mobile hosts may become failure. The security is an important criterion to keep VANETS threat free. So therefore this paper presents an efficient Quality Enhancement scheme to improve handoff performance in Vehicular Adhoc Networks. A Recovery scheme and security features is also proposed in this paper. Our Proposed scheme periodically collects the states of the base stations by Access Point Co ordinator (APC) and predicts the intensity of the traffic at the next moment. Moreover the recovery scheme recovers the lost information if at all the the handoff scheme fails.
WiSel: a mixed initiative approach for widget selection

Adapting graphical user interface is extremely important to streamline interaction and to meet changing requirement of the context of use. This shortcoming is common and repetitive for GUI and necessitates further considerations with the growing complexity of interactive systems. However most of works addressing adaptation capitalize on design-time and predefined rules, which do not always meet real users preferences and expectations during use. WiSel is a tool-supported approach based on our framework for supporting Intelligent Agile Runtime Adaptation. It consists on a mixing of both knowledge-based (adaptive) and user-drived (adaptable) UI customization. Both adaptation behaviors work together on considering runtime end-users preferences to improve adaptation of interaction object at runtime. We investigated how end-users involvement can improve widget selection and enhance the user satisfaction. A preliminary pilot study was conducted to demonstrate the feasibility of our approach and its impact on usability in term of controllability, transparency and predictability.
Study of the Propagation of IEMI Signals Along Power and Communication Lines

This note studies the propagation of IEMI (Intentional Electromagnetic Interference) signals along power/communication cables. Specifically, the attenuation and distortion of the IEMI signals resulting from conductive and dielectric losses are studied. The presented analysis allows the evaluation of the required propagation distance at which the attenuation and distortion of IEMI signals are such that traditional SPDs can be effectively used to protect sensitive devices connected at the end of the lines.
Learning failure in information systems development

Abstract. Abstract. Information systems development is a high‐risk undertaking, and failures remain common despite advances in development tools and technologies. In this paper, we argue that one reason for this is the collapse of organizational intelligence required to deal with the complexities of systems development. Organizations fail to learn from their experience in systems development because of limits of organizational intelligence, disincentives for learning, organizational designs and educational barriers. Not only have many organizations failed to learn, but they have also learned to fail. Over time they accept and expect poor performance while creating organizational myths that perpetuate short‐term optimization. This paper illustrates learning failure in systems development and recommends tactics for overcoming it.
In the Mix: exploring urban Black Youth Culture in desegregated life sciences classrooms.

Through an anthropological approach, in the form of a focused-ethnography, this study explored urban Black youth culture (UBYC), in the life sciences classrooms of a desegregated former model C school. In the exploration of UBYC the study was guided by the four research questions the address of which helped explain the context in which UBYC was created by UBY; the nature of this created culture and lastly; how and why it influenced the teaching and learning of life sciences. This study was trellised on a conflated conceptual and analytical framework which informed the nature of the research questions, research design and methodology. Such a conflated framework included: Schein’s (2004) organisational culture model for cultural analysis; Ryan and Deci (2000) Self-Determination Theory of Motivation; Bourdieu’s (1991) social imagery and Foucault’s (1997) notions of power. Using multiple methods, qualitative data were collected over a six month period. Data were analysed and interpreted against the analytical and conceptual frames and a report was compiled. Key findings of this study included the identification of the culpability of the context as structured by players in the life sciences classrooms in the creation of UBYC. Such contextual shortcomings included: classrooms in which culturally responsive pedagogy was not operationalised, classrooms contexts which were falling short in addressing learners’ needs for autonomy, competency and connectedness, and lastly, operationalisation of power in ways that escalated classroom conflicts. Schein’s (2004) model was used to decipher life sciences teachers’ assumptions. Through knowledge of the teachers’ assumptions and observation of their classroom practices, the context in which UBYC was created was established. It was from this understanding that UBY assumptions were deciphered. The deciphering of UBY cultural assumptions provided for the interrogation of how UBYC was influencing, and why it was influencing the teaching and learning of life sciences. It was found that UBYC enabled UBY to trivialise life sciences as a discipline, speak disparagingly about their teachers, disrupt classroom proceedings, sometimes openly defy or aggressively engage with their life sciences teachers. UBYC enabled UBY to perform such enactments as it allowed them to feel superior, powerful, connected and competent. It is envisaged that findings of this study would provide a lens for viewing contemporary classrooms. This perception is critical in deciphering and explaining phenomena that may be perceived as indiscipline and behavioural challenges. This study culminated with the development of a model for cultural studies in classroom settings. It was my view that such
A distributed approach to retrieving JPEG pictures in portable hypermedia documents

In this paper, we single out one of the problem aspects of multimedia: how can one efficiently store high-quality picture information in a manner that does not make its retrieval characteristics incompatible with the needs of a (distributed) multimedia application. These needs include: timely access to data, predictable access to common (i.e., network) resources and ease in user specification of information. Our approach to solving this problem is to define adaptive data objects that adjust the amount and type of information given to an application as a function of resource availability. The key to our approach is that we transparently adapt the information presented to the application based on a set of pre-specified conditions that were defined by the application at author time. We discuss this work in the context of a parallel JPEG image decoder that provides adaptive images (with respect to data content and image representation) based on a transparent client/server negotiation scheme. Our work is based on the Amsterdam Multimedia Framework (AMF), a partitioning of control operations for supporting distributed multimedia. The parallel JPEG algorithm, AMF and the negotiated control algorithm are explained.
Compounded Regularization and Fast Algorithm for Compressive Sensing Deconvolution

Compressive Sensing Deconvolution (CS Deconvolution) is a new challenge problem encountered in a wide variety of image processing fields. A compound variational regularization model which combined total variation and curve let-based sparsity prior is proposed to recovery blurred image from compressive measurements. We propose a novel fast algorithm using variable-splitting and Dual Douglas-Rachford operator splitting methods. Experiments demonstrate our proposed algorithm can obtain high-resolution data from highly incomplete measurements.
Multiplexing siRNAs to compress RNAi-based screen size in human cells

Here we describe a novel strategy using multiplexes of synthetic small interfering RNAs (siRNAs) corresponding to multiple gene targets in order to compress RNA interference (RNAi) screen size. Before investigating the practical use of this strategy, we first characterized the gene-specific RNAi induced by a large subset (258 siRNAs, 129 genes) of the entire siRNA library used in this study (∼800 siRNAs, ∼400 genes). We next demonstrated that multiplexed siRNAs could silence at least six genes to the same degree as when the genes were targeted individually. The entire library was then used in a screen in which randomly multiplexed siRNAs were assayed for their affect on cell viability. Using this strategy, several gene targets that influenced the viability of a breast cancer cell line were identified. This study suggests that the screening of randomly multiplexed siRNAs may provide an important avenue towards the identification of candidate gene targets for downstream functional analyses and may also be useful for the rapid identification of positive controls for use in novel assay systems. This approach is likely to be especially applicable where assay costs or platform limitations are prohibitive.
Hispanic Representation in Special Education: Patterns and Implications

The inaccurate placement of minority students in special education programs can take various forms including overrepresentation, underrepresentation, and misidentification. First, overrepresentation occurs when the percentage of minority students in special education programs is greater than that in the school population as a whole. Second, underrepresentation occurs when students with disabilities are not identified and do not receive appropriate services. Last, misidentification occurs when students with disabilities are identified as having a disability different from the one they actually have (G. Meyer & J. M. Patton, 2001; C. Y. Wilkinson, A. A. Ortiz, P. M. Robertson, & M. I. Kushner, 2006). To answer the question of whether Hispanic children are disproportionately represented in special education, the author conducted a literature review in a systematic manner using computerized databases. In addition, the author provides an integrative review of the literature on Hispanic representation in special education, presents specific issues that complicate the accurate identification of Hispanic or Latino, and identifies implications for practice.
A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic

The increasing popularity of specialized Internet-connected devices and appliances, dubbed the Internet-of-Things (IoT), promises both new conveniences and new privacy concerns. Unlike traditional web browsers, many IoT devices have always-on sensors that constantly monitor fine-grained details of users' physical environments and influence the devices' network communications. Passive network observers, such as Internet service providers, could potentially analyze IoT network traffic to infer sensitive details about users. Here, we examine four IoT smart home devices (a Sense sleep monitor, a Nest Cam Indoor security camera, a WeMo switch, and an Amazon Echo) and find that their network traffic rates can reveal potentially sensitive user interactions even when the traffic is encrypted. These results indicate that a technological solution is needed to protect IoT device owner privacy, and that IoT-specific concerns must be considered in the ongoing policy debate around ISP data collection and usage.
Approximate Bayesian inference as a form of stochastic approximation: A new consistency theory with applications

Approximate Bayesian inference is a powerful methodology for constructing computationally efficient statistical learning mechanisms in problems where incomplete information is collected sequentially. Approximate Bayesian models have been developed and applied in a variety of different domains; however, this work has thus far been primarily computational, and convergence or consistency results for approximate Bayesian estimators are largely unavailable. We develop a new consistency theory for these learning schemes by interpreting them as stochastic approximation (SA) algorithms with additional “bias” terms. We prove the convergence of a general SA algorithm of this type, and apply this result to demonstrate, for the first time, the consistency of several approximate Bayesian methods from the recent literature.