Miracle-dz/UniFAR-FTUs huggingface dataset:
AMIGOS: A dataset for Mood, personality and affect research on Individuals and GrOupS

We present a database for research on affect, personality traits and mood by means of neuro-physiological signals. Different to other databases, we elicited affect using both short and long videos in two settings, one with individual viewers and one with groups of viewers. The database allows the multimodal study of the affective responses of individuals in relation to their personality and mood, and the analysis of how these responses are affected by (i) the individual/group setting, and (ii) the duration of the videos (short vs long). The data is collected in two experiments. In the first one, 40 participants watched 16 short emotional videos while they were alone. In the second one, the same participants watched 4 long videos, some of them alone and the rest in groups. Participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR), were recorded using wearable sensors. Frontal, full-body and depth videos were also recorded. Participants have been profiled for personality using the Big-five personality traits, and for mood with the baseline Positive Affect and Negative Affect Schedules. Participants emotions have been annotated with both, self-assessment of affective levels (valence, arousal, control, familiarity, like/dislike, and selection of basic emotion) felt by the participants during the first experiment, and external-assessment of participants' levels of valence and arousal for both experiments. We present a detailed correlation analysis between the different scores of personality, mood and affect. We also present baseline methods and results for single-trial classification of valence and arousal, and for single-trial classification of personality traits, mood and social context (alone vs group), using EEG, GSR and ECG and fusion of modalities for both experiments. The database has been made publicly available.
Advertising Standards and Online Advertising

The technological transformation and automation of digital content delivery has revolutionized the advertising industry. Increased reliance on automation has also led to requirements for standardization of advertising formats. This paper examines how the memorability of banner advertising changed with the introduction of new standards regularizing the size of display advertising. Using data from randomized field tests, we find evidence that for most ads, ad effectiveness falls as a result of standardization. The decline is also weaker when a standardized ad is the only ad on the page, and when the ads appear to be more original (such as ads created by an ad agency), so a likely explanation is that standardization makes it harder for basic ads to distinguish themselves from their competition.
Identification of Four Entamoeba histolytica Organellar DNA Polymerases of the Family B and Cellular Localization of the Ehodp1 Gene and EhODP1 Protein

We report the identification of a family of four active genes (Ehodp1, Ehodp2, Ehodp3, and Ehodp4) encoding putative DNA polymerases in Entamoeba histolytica, the protozoan parasite responsible of human amoebiasis. The four Ehodp genes show similarity to DNA polymerases encoded in fungi and plant mitochondrial plasmids. EhODP polypeptides conserve the 3′-5′ exonuclease II and 5′-3′ polymerization domains, and they have the I, II, and III conserved boxes that characterize them as DNA polymerases of family B. Furthermore, we found in EhODP polymerases two novel A and B boxes, present also in DNA polymerases encoded in fungi mitochondrial plasmids. By in situ PCR, Ehodp1 gene was located in nuclei and in DNA-containing cytoplasmic structures. Additionally, using polyclonal antibodies against a recombinant rEhODP1-168 polypeptide, and confocal microscopy, EhODP1 was located in cytoplasmic DNA-containing structures.
Introducing experiential knowledge platform: A smart decision supporter for field experts

Experiential knowledge is knowledge obtained through reflection on experience. In case of experiential knowledge within a specialized domain, this knowledge is strengthened over time as a field expert accumulates more experience in the chosen field. However, it is unfortunate that the knowledge is often confined within each individual in implicit form and it is hardly well-managed by an organization. Although there are several systems designed to acquire and exploit experiential knowledge, escalating maintenance costs pose serious challenges to their adoption and continuous use. In this paper, we propose a new knowledge-based system that acquires experiential knowledge through natural interactions with domain experts and keeps it growing by adding specialization rules, thereby reducing maintenance costs substantially. We also present the overall flow of how the acquired knowledge is processed and applied to decision supporting process, particularly in diagnosing potential diseases from blood tests.
The Formation and Performance of Auxetic Textiles

Auxetic textiles belong to a class of extraordinary materials that become fatter when stretched and are increasingly attaining some prominence in many applications of technical textiles [1]. The envisaged production route will be to first synthesize the auxetic polymers, then to produce the yarn [2,3] and the composites from these polymers [4, 5]. Sustained efforts to fabricate auxetic fabric structures are limited. Several geometrical configurations have been proposed [5] but none has been engineered into functional auxetic textile structures. In this project, we are combining our knowledge of geometry and fabric structural characteristics to engineer auxetic textiles and determine the properties of such auxetic structures. In spite of its exceptional formability, a knit structure is characterized by allegedly poor in-plane mechanical performance compared to more conventional woven composites and materials [6]. The knitting process is an extremely versatile manufacturing process and an enormous range of knit structures exists. This does not simplify the selection of an appropriate knit structure. For example, in composite materials, the selection of a knit structure is based on three technical criteria. First, the deformability of the knit fabric is important because it determines the shapes that can be formed with it. The second selection criterion for knit fabrics consists of the resulting mechanical (and other) properties of the knit fabric composite. The third criterion for selection of a knit structure is the hand. [7]. We have shown that textiles with net structures are the more preferred materials and geometry for composites [8]. http://www.ntcresearch. org/pdf-rpts/AnRp06/F06-MD09A6.pdf Indeed, warp knitting technology provides the most suitable knowhow for net structures and offers major advantages in its versatility and high production speed. However, the set-up costs are considerable because the knitting machine has to be equipped with one or two needle beds and many guide bars. Nevertheless, a huge variety of knit structures can be produced and no other technology can match warp knitting technology in the production of net structures. Thus, with the right stitch construction and proper material selection, it is possible to knit square, rectangular, rhomboidal, hexagonal or almost round shape [9].
DOCLIB: a software library for document processing

Most researchers would agree that research in the field of document processing can benefit tremendously from a common software library through which institutions are able to develop and share research-related software and applications across academic, business, and government domains. However, despite several attempts in the past, the research community still lacks a widely-accepted standard software library for document processing. This paper describes a new library called DOCLIB, which tries to overcome the drawbacks of earlier approaches. Many of DOCLIB's features are unique either in themselves or in their combination with others, e.g. the factory concept for support of different image types, the juxtaposition of image data and metadata, or the add-on mechanism. We cherish the hope that DOCLIB serves the needs of researchers better than previous approaches and will readily be accepted by a larger group of scientists.
Human head detection using Histograms of Oriented optical flow in low quality videos with occlusion

Video detection and tracking of humans in large events is a non-trivial task. The tracked information is useful in calculating crowd behavior parameters such as density, speeds and paths that are critical in making automated decision about crowd behavior. During occluded scenarios, it is very common to have only head and shoulder visible in the videos and the rest of the body is blocked by other people or objects. Although there is wealth of information in literature, tracking in occluded high density environments is not well addressed. In this paper, we aim to detect head and shoulder of people by hybrid motion and visual features in a low quality video with occlusion and cluttered environment. We propose an improved version of histogram of oriented optical flow (HOOF) called integral HOOF. The HOOF is not only shown to be a discriminative feature in low quality video, but also an alternative way to segment moving objects in a video. HOOF and Histogram of Oriented Gradient (HOOG) features are extracted from a real world surveillance video, and Support Vector Machine classifier is trained to detect heads and shoulders. Experimental result shows the HOOF has high precision, recall and accuracy on detecting heads and shoulders.
A novel spectral coding in a large graph database

Retrieving related graphs containing a query graph from a large graph database is a key issue in many graph-based applications, such as drug discovery and structural pattern recognition. Because sub-graph isomorphism is a NP-complete problem [4], we have to employ a filter-and-verification framework to speed up the search efficiency, that is, using an effective and efficient pruning strategy to filter out the false positives (graphs that are not possible in the results) as many as possible first, then validating the remaining candidates by subgraph isomorphism checking. In this paper, we propose a novel filtering method, a spectral encoding method, i.e. GCoding. Specifically, we assign a signature to each vertex based on its local structures. Then, we generate a spectral graph code by combining all vertex signatures in a graph. Based on spectral graph codes, we derive a necessary condition for sub-graph isomorphism. Then we propose two pruning rules for sub-graph search problem, and prove that they satisfy the no-false-negative requirement (no dismissal in answers). Since graph codes are in numerical space, we take this advantage and conduct efficient filtering over graph codes. Extensive experiments show that GCoding outperforms existing counterpart methods.
Possible Future Monoclonal Antibody (mAb)-Based Therapy against Arbovirus Infections

More than 150 arboviruses belonging to different families are known to infect humans, causing endemic infections as well as epidemic outbreaks. Effective vaccines to limit the occurrence of some of these infections have been licensed, while for the others several new immunogens are under development mostly for their improvements concerning safety and effectiveness profiles. On the other hand, specific and effective antiviral drugs are not yet available, posing an urgent medical need in particular for emergency cases. Neutralizing monoclonal antibodies (mAbs) have been demonstrated to be effective in the treatment of several infectious diseases as well as in preliminary in vitro and in vivo models of arbovirus-related infections. Given their specific antiviral activity as well-tolerated molecules with limited side effects, mAbs could represent a new therapeutic approach for the development of an effective treatment, as well as useful tools in the study of the host-virus interplay and in the development of more effective immunogens. However, before their use as candidate therapeutics, possible hurdles (e.g., Ab-dependent enhancement of infection, occurrence of viral escape variants) must be carefully evaluated. In this review are described the main arboviruses infecting humans and candidate mAbs to be possibly used in a future passive immunotherapy.
Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients

The objective of this study was to investigate the validity of an objective gait measure for assessment of different motor states of advanced Parkinson's disease (PD) patients. Seven PD patients performed a gait task up to 15 times while wearing sensors on their upper and lower limbs. Each task was performed at specific points during a test day, following a single dose of levodopa-carbidopa. At the time of the tasks the patients were video recorded and three movement disorder experts rated their motor function on three clinical scales: a treatment response scale (TRS) that ranged from −3 (very bradykinetic) to 0 (ON) to +3 (very dyskinetic), a dyskinesia score that ranged from 0 (no dyskinesia) to 4 (extreme dyskinesia), and a bradykinesia score that ranged from 0 (no bradykinesia) to 4 (extreme bradykinesia). Raw accelerometer and gyroscope data of the sensors were processed and analyzed with time series analysis methods to extract features. The utilized features quantified separate limb movements as well as movement symmetries between the limbs. The features were processed with principal component analysis and the components were used as predictors for separate support vector machine (SVM) models for each of the three scales. The performance of each model was evaluated in a leave-one-patient out setting where the observations of a single patient were used as the testing set and the observations of the other 6 patients as the training set. Root mean square error (RMSE) and correlation coefficients for the predictions showed a good ability of the models to map the sensor data into the rating scales. There were strong correlations between the SVM models and the mean ratings of TRS (0.79; RMSE=0.70), bradykinesia score (0.79; RMSE=0.47), and bradykinesia score (0.78; RMSE=0.46). The results presented in this paper indicate that the use of wearable sensors when performing gait tasks can generate measurements that have a good correlation to subjective expert assessments.
Vision-based turning movement counting at intersections by cooperating zone and trajectory comparison modules

This work presents a vision-based vehicle tracking system with an improved method for automatically obtaining turning movement counts at intersections. Improved accuracy is obtained for intersections of varying difficulty through cooperation between complimentary counting modules. When vehicle tracking is robust, a typical zone comparison module quickly accounts for predefined image regions. When vehicle tracking is broken due to occlusion or noise, a trajectory comparison module utilizing the least common subsequence distance is able to count the broken trajectories using the typical scene paths. Experimental evaluations during evening peak hours at two different intersections show an 15% average improvement at two intersections where the trajectory comparison module is in use 22% of time. Finally, counts of five working days are compared for another intersection in 15 minute intervals to highlight the system's operational utility for turning behavior analysis at high temporal resolution.
Generating a compiler for a lazy language by partial evaluation

Compiler generation is often emphasized as being the most important application of partial evaluation. But most of the larger practical applications have, to the best of our knowledge, been outside this field. Expecially, no one has generated compilers for languages other than small languages. This paper describes a large application of partial evaluation where a realistic compiler was generated for a strongly typed lazy functional language. The language, that was called BAWL, was modeled after the language in Bird and Wadler [BW88] and is a combinator language with pattern matching, guarded alternatives, local definitions and list comprehensions. The paper describes the most important techniques used, especially the binding time improvements needed in order to get small and efficient target programs. Finally, the performance of the compiler is compared with two compilers for similar languages: Miranda and LML.
Lowering the barrier to applying machine learning

Machine learning algorithms are key components in many cutting edge applications of computation. However, the full potential of machine learning has not been realized because using machine learning is hard, even for otherwise tech-savvy developers. This is because developing with machine learning is different than normal programming. My thesis is that developers applying machine learning need new general-purpose tools that provide structure for common processes and common pipelines while remaining flexible to account for variability in problems. In this paper, I describe my efforts to understanding the difficulties that developers face when applying machine learning. I then describe Gestalt, a general-purpose integrated development environment designed the application of machine learning. Finally, I describe work on developing a pattern language for building machine learning systems and creating new techniques that help developers understand the interaction between their data and learning algorithms.
14.1 A 0.9V 20.9dBm 22.3%-PAE E-band power amplifier with broadband parallel-series power combiner in 40nm CMOS

This paper reports a fully integrated 40nm CMOS PA that utilizes a broadband parallel-series power combiner to achieve an output power (POUT) of 20.9dBm with more than 15GHz small-signal 3dB bandwidth (BW-3dB) and 22% PAE at 0.9V supply. The in-band variation of P1dB is only ±0.25dB. This silicon-based PA covers both 71-to-76GHz and 81-to-86GHz bands with uniform gain, output power and PAE.
Fatty acids in the infant diet: size matters

The long-term metabolic health outcomes of breast-fed v. formula-fed infants have received considerable attention and, although still controversial, there have been some suggestions that breast-feeding may be protective against the development of obesity. Although it is challenging in humans to separate the effects of breast-feeding per se from those of other socio-economic/lifestyle factors with which extended breast-feeding is closely entwined, animal data support the concept that there are biological links between being reared on artificial milk and being reared on natural suckling. The question then becomes – what are the drivers? The principal hypothesis, to date, has been related to differences in the composition of breast milk v. infant formula, particularly in its fatty acid content. Specifically, it has been suggested that the higher n-3 polyunsaturated fat (PUFA) and lower n-6 PUFA contents in breast milk compared with the contents in infant formula are associated with lower rates of adipogenesis/lipogenesis in the developing neonate, giving rise to lower numbers of adipocytes with a lower capacity for fat storage. The paper by Oosting et al. published in this issue, however, suggests that there is another factor that needs to be considered – namely, the physical structure of the lipids in human milk compared with that of the lipids in infant formula. Oosting et al. prepared two diets with the same fatty acid composition, which had lipid droplets resembling either those in human breast milk (large lipid droplets surrounded by a biological membrane, Concept IMF) or those in infant formula (smaller lipid droplets with no biological membrane, Control IMF). They then fed these diets to rat offspring from postnatal day 16 until young adulthood. All offspring were then fed a Western-style diet until postnatal day 98. They found that rats fed the Concept IMF in early life gained less weight and less fat on the Western-style diet than the controls – indicating that they were less susceptible to diet-induced obesity. Importantly, this was driven almost exclusively by a reduction in lipogenesis, as indicated by the lower expression of lipogenic transcription factors and reduced size of the adipocytes. Although changes in the fatty acid composition of the earlylife diet have been shown to programme the function of adipocytes, this is the first time that it has been reported that this is associated with differences in the structural properties of lipids consumed in early life. This adds a new element to the growing body of literature linking early nutritional exposures to increased obesity risk and highlights the sensitivity of the developing adipocytes to the manipulations of the infant diet.
Studies of Inverted Organic Solar Cells Fabricated by Doctor Blading Technique

Over the last few decades, bulk-heterojunction organic photovoltaic devices comprising an intimately mixed donor-acceptor blend have gained serious attention due to their potential for being cheap, light weight, flexible and environmentally friendly. In this thesis, APFO-3/PCBM bulk-heterojunction based organic photovoltaic devices with an inverted layer sequence were investigated systematically. Doctor blade coating is a technique that is roll-to-roll compatible and cost efficient and has been used to fabricate the solar cells. Initial studies focused on optimization of the electrodes. A thin film of the conductive polymer PEDOT:PSS was chosen to be the transparent anode. Different PEDOT:PSS films with respect to the film thickness and deposition temperature were characterized in terms of conductivity and transmission. Decent conductance and transmittance were obtained in the films deposited with wet film thickness setting of 35 μm, The cathode was fabricated from a metal bilayer comprising Al and Ti with an area about 1 cm2, and the best-working cathodes contained a 70 nm thick Al layer covered by a thin Ti layer of about 10 -15 nm. Optimized coating temperature and wet film thickness settings for the active layer and PEDOT:PSS layer were experimentally determined. The highest efficiency of the APFO-3/PCBM based inverted solar cells fabricated by doctor blading was 0.69%, which exceeded the efficiency of spin-coated inverted cells. A higher efficiency (0.8 %) was achieved by adding a small amount of high molecular weight polystyrene to the active layer. Morphological changes after adding of the polystyrene were observed by optical microscopy and AFM. A coating temperature dependent phase separation of the APFO-3/PCBM/polystyrene blend was found.
Optimization of Ultrasonic Extraction of Phenolic Antioxidants from Green Tea Using Response Surface Methodology

Response surface methodology (RSM) has been used to optimize the extraction conditions of antioxidants with relatively low caffeine content from green tea by using ultrasonic extraction. The predicted optimal conditions for the highest antioxidant activity and minimum caffeine level were found at 19.7% ethanol, 26.4 min extraction time, and 24.0 °C extraction temperature. In the predicted optimal conditions, the experimental values were very close to the predicted values. Moreover, the ratio of (EGCg + ECg)/EGC was identified a major factor contributing to the antioxidant activity of green tea extracts. In this study, ultrasonic extraction showed that the ethanol concentration and extraction time used for antioxidant extraction could be remarkably reduced without a decrease in antioxidant activity compared to the conventional extraction conditions.